How to set up an APA format paper in Google Docs
- How to use Google Docs' APA format templates
How to write an APA format paper in Google Docs using a template or other built-in features
- You can write an APA formatted paper in Google Docs using its built-in tools or a template.
- The basics of APA 7 format include double-spaced lines, a running header, and a title page — all of which can be done in Google Docs.
- Google Docs' templates page includes pre-made APA 6 and APA 7 documents you can use as well.
While some students write in MLA format, others write in APA format. APA — short for American Psychological Association — is a standardized format for writing academic papers, especially in the fields of sociology, psychology, and other behavioral or social sciences. It has specific rules for what your essays should look like, and how they should be structured.
APA format has changed a few times over the decades (right now we're on APA Seventh Edition, or "APA 7"), but the basics have stayed the same. And no matter which version of APA format you're using, you can set it all up using Google Docs.
Here's how to make an APA essay in Google Docs, either manually or using a template.
Like other style guides, APA format has a variety of rules and standards. Here are the most important guidelines for structuring your paper, along with tips on how to meet those guidelines in Google Docs.
- The font needs to be readable and consistent.
APA isn't strict about what font you should use, or even what size it should be. It just needs to be legible, and you need to use the same font throughout your entire paper (with exceptions for figures, computer code, and footnotes). Some common choices are 12-point Times New Roman, 11-point Arial, and 11-point Calibri.
You can change your font and font size using the toolbar at the top of the screen. If you're trying to change text that you've already written, just be sure to highlight it first.
- Your entire document needs to have one-inch margins and double-spaced lines.
All Google Docs documents have one-inch margins by default, so you probably don't need to worry about that. If you want to double-check or change them anyway, you can change the margins using the Page Setup menu or ruler feature .
Meanwhile, you can enable double-spacing with the Line & paragraph spacing menu in the toolbar above your document. Highlight all the text in your document, then select Double in this menu to turn on double-spacing .
- Every page needs a header with the paper's title in the top-left, and the page number in the top-right.
Google Docs lets you add headers to any page. You can add automatic page numbers through the Insert menu , and then double-click the header to add your title on the left if needed.
Remember that they need to be the same font and font size as the rest of your paper.
- Your paper needs a title page with your name, paper title in bold, "institutional affiliation," and more.
Probably the most important part of an APA paper is the title page. It needs to include the paper's title in bold, your name, and your "institutional affiliation" — the school or organization that you're writing for. If you're a student, you also need to add the course number and name, your instructor's name, and the due date.
All this information should be centered in the upper-half of the first page. You can find Google Docs' alignment options in the toolbar at the top of the page. Highlight your text and select Center align in this menu to center everything.
- Your paper should end with a References page, and each entry should be written with a hanging indent.
The last section of your paper is the References page. Make sure to put it on a new page (or pages, depending on how many you have to cite).
The word "References" should be centered and bolded on the very first line of the page. You can center the words using the alignment options mentioned above, and bold it by clicking the B icon .
List all your references in alphabetical order and use the ruler to give each one a hanging indent — in other words, every line after the first needs to be indented .
Your citations need hanging indents, which you can make with the ruler tool. Google; William Antonelli/Insider
How to use google docs' apa format templates.
While you can format your paper manually, Google Docs also offers two different APA templates — one for APA 7, and another for APA 6. These templates will let you meet most of the APA guidelines right away, but you'll probably still need to change some of it.
To use one of these templates:
1. Head to the Google Docs homepage and click Template gallery in the top-right.
2. Scroll down the templates page until you reach the Education section. In this section, click either Report [APA 6th ed] or Report [APA 7th ed] .
3. A page will open with an APA format paper already written in fake Lorem Ipsum language. Most of the formatting is there, so you just need to replace the pre-written words with your own.
You can find these templates in the mobile app by tapping the plus sign icon in the bottom-right, and then selecting Choose template .
- Main content
How to Style Your Paper with APA Format in Google Docs
- Last updated November 15, 2023
If you’re a student or working in the academic field, you’re probably somewhat familiar with the APA format. However, formatting your paper according to APA guidelines can be daunting, especially if you’re using a tool like Google Docs.
But fear not! In this article, we’ll teach you how to use APA format in Google Docs and access its built-in APA templates to save you time and effort in the long run.
Let’s dive in!
Table of Contents
What Is APA Format?
APA stands for American Psychological Association. From its name, it’s easy to guess that this style has become the standard for many disciplines, including psychology, education, and social sciences.
The APA Style is a standard format used in essays, research, and other forms of academic writing.
Related : How to Do MLA Format on Google Docs [Step-by-Step Guide]
How to Set Up APA Format in Google Docs
Before starting the writing process, it’s essential to customize the appearance of your document. This ensures that every important section in an APA-style paper is present. As of writing, APA is in its seventh edition (APA 7). Therefore, we’ll use this version in the following guide.
Here’s how to do APA format in Google Docs.
Step 1: Configure Margin Settings
By default, a new document in Google Docs has one-inch margins on all sides. If you’ve changed your default settings at some point in the past, you’ll need to modify them for APA.
- Go to “File,” then select “Page setup.”
- Ensure that all margins are set at 1 inch (or in the margin size specified by your instructor).
- Click “OK” when done.
Step 2: Add Page Headers
- In the drop-down menu bar, select “ I nsert” > “Headers & footers” > “Header.”
- If you’re writing the paper for professional use, type the title in all caps (as a running head ). If not, skip step three.
- Highlight the page header and select “Times New Roman,” size 12 as the font.
- Under the Header menu, click on the “Options” drop-down and choose “Page numbers.”
- Ensure that the value in the “Start at” field is 1. Click on the “Apply” button to insert the pagination.
- Place your cursor at the immediate left of the page number. Press the “Tab” key and/or the spacebar on your keyboard to flush the page number to the right.
Step 3: Set up the APA Format for Title Page in Google Docs
- Click on any part of your document.
- Change the font style by clicking on the “Font” drop-down menu and selecting “Times New Roman.”
- Adjust the font size to 12 using the “Font size” option in the Google Docs toolbar.
- Click on the “Line & paragraph spacing” button (denoted by an up-down arrow with three horizontal lines). Select “Double.”
- Press the “Enter” or “Return” key on your keyboard three to four times.
- Click on “Align” > “Center align” in the toolbar to flush the text to the middle of the page.
- Type your paper title and make it bold by pressing “Ctrl” + “B” (or “Cmd” + “B” for Mac) on your keyboard.
- Alternatively, highlight the title and click the “B” (Bold) button on the toolbar at the top.
- Add a new line, then type the name of the author(s) below it.
- For students : Author’s school, course number and name, name of the instructor, and assignment due date.
- For professionals : Author’s affiliation (where the research was conducted), notes from the author, and ORCiD link .
- To start a new page, select “Insert” > “Break” > “Page break.”
Step 4: Insert an Abstract Page
An abstract page presents the overall gist of your paper. It contains both the summary and a list of keywords related to your topic. Note that this is only important for professional papers.
To insert an abstract page, simply follow the steps below.
- On a new page after the title page, type “Abstract” and select “Align” > “Center align” in the toolbar.
- Make it bold by pressing “Ctrl” + “B” (or “Cmd” + “B” for Mac).
- Press “Enter” to start a new line.
- Enable “Left align” and begin typing your abstract.
Step 5: Type the Full Paper Title & Start Writing
Having laid out the basic formatting of your paper, you can now begin writing your content. On the first line of a new page, you’ll have to enter the full title of your work. This should be bolded, centered, and using an APA-style title case .
When it comes to the body of your paper or dissertation, there are a few more elements to remember.
Apply the Correct Paragraph Format
When using the APA style, your paragraphs should be aligned to the left margin. Each paragraph should also start with a 0.5-inch indentation . In Google Docs, pressing the “Tab” key on your keyboard should produce a half-inch indentation by default.
If you’re citing original text from another source with more than 40 words, you should use a “block quotation.” This means the whole block or paragraph is indented ½ inch to the right (but remains left-aligned).
Related : Easily Insert a Google Sheet Into Google Docs [2023 Guide]
Use In-Text Citations Properly
When getting information from other references, make sure to use in-text citations. You can do this in two ways:
- (Author’s surname, publication year, and page number): This is called a parenthetical citation . You can insert it right after a quote or at the end of a sentence. The page number is important if you’re citing specific lines from another source. But you can remove it if you’re citing the summary of an entire paper.
- [Author] reported that…(Publication year): This is called a narrative citation . Here, the surname of the author is part of the paragraph itself. You only need to enclose the publication year in parentheses.
How to Format References for APA Style
You have to give credit to every source used in your APA research and paper. This allows your instructor or reader to verify whether certain information in your work is true. Therefore, you must have a References page at the end of your document.
Here’s how to set it up.
- On a new page, type the word “References” on the first line.
- Put it in center alignment and make it bold.
- Start a new line and input the correct format for your source type .
- After listing your references, alphabetize them based on the surnames of the authors.
- Highlight your reference list.
- Go to “Format” > “Align & indent” > “Indentation options.”
- Under “Special indent,” select “Hanging” from the drop-down menu.
- Click on the “Apply” button.
How to Use the APA Google Docs Templates
Given the common use of the APA format in academia, it’s common to find templates designed for it. Google Docs itself offers two variants: APA 6th Ed. and APA 7th Ed. (the latest edition).
Here’s how you can set them up for your use.
- Launch your browser and go to the Google Docs homepage .
- Click on “Template Gallery.”
- Under the “Education” category, select “Report” with the words “APA 6th Ed.” or “APA 7th Ed.” below it (depending on what your instructor requires).
You can also access the templates from a blank document you created. To do this, click on “File” > “New” > “From template.” This will redirect you to the same Template Gallery. Select any of the two APA formats to proceed.
Doing the steps above would open an APA format template in Google Docs. All you have to do is insert your content.
It’s important to note that APA formatting for professional and student papers differs slightly. This is noticeable, especially when making the APA cover page in Google Docs . These templates have parentheses that say “for professional papers” and “for student papers.” Select what applies to you and delete the other unnecessary parts of the template.
Get Even More APA Format Tips and Templates!
By following this guide to using APA format in Google Docs, you’ll ensure that papers meet your educational institution’s formatting requirements (while giving your work a professional and polished look).
Need more assistance with APA? Not to worry: Udemy’s got plenty of APA formatting courses to give you a leg-up!
Looking for powerful templates to improve your work output? You can find them on our Gumroad page ! Check out our huge list of templates and get 50% off by using the code “SSP .”
- How to Make Columns in Google Docs [Complete Guide]
- How to Highlight in Google Docs [Step-by-Step Guide]
- How to Save a Document in Google Docs: A Quick Guide
- 20 Best Handwriting Fonts on Google Docs to Add a Personal Touch
Most Popular Posts
How To Highlight Duplicates in Google Sheets
How to Make Multiple Selection in Drop-down Lists in Google Sheets
Google Sheets Currency Conversion: The Easy Method
A 2024 guide to google sheets date picker, related posts.
How to Zoom Out in Google Sheets and Zoom Back In [Easy]
- Sumit Bansal
- July 3, 2024
- May 16, 2024
- May 2, 2024
How to Insert a Google Sheets Hyperlink in 5 Seconds
- Chris Daniel
- April 15, 2024
Thanks for visiting! We’re happy to answer your spreadsheet questions. We specialize in formulas for Google Sheets, our own spreadsheet templates, and time-saving Excel tips.
Note that we’re supported by our audience. When you purchase through links on our site, we may earn commission at no extra cost to you.
Like what we do? Share this article!
Formatting Papers: Google Docs
- Basic Formatting
- TU Writing Resources
- Sample Papers
- Check out the Basic Formatting page for quick, easy instructions on how to format your paper using Google Docs.
If you don't already have a Google account set up, click this link to create a free account. With a Google account you can access the professional suite for Google Docs, Sheets, Presentations, and more. You can also create and customize Gmail, YouTube, and Google Maps profiles and store your personalizations. Google is free to sign up with and you can access your documents from any public computer you log into, including the computers in the Library computer labs. Just make sure you always sign off when you're finished! Otherwise your personal information can be accessed by anyone.
For more help using and formatting Google Docs, check out this Essential Training and other helpful tutorial courses available on Linkedin Learning.
Linkedin Learning is a professional social network that has recently expanded to maintain and develop a wide variety of video tutorials and online courses to foster continued learning for business, higher education, and government professions. Their tutorials and courses on digital technologies and software are particularly informative in this ever growing Digital Age. To access full content on Linkedin Learning sign up for an account.
- << Previous: Basic Formatting
- Next: Basic Formatting >>
- Last Updated: Aug 13, 2024 8:11 PM
- URL: https://libguides.thomasu.edu/papers
Research Paper Formatting Tips & Tricks
- Formatting Basics -- Word
Formatting Basics -- Google Docs
- Set Default Formatting in Word & Google Docs
- Setting the Font
- Setting the Margins
- Setting up Double Spacing
- Set Page Numbers
- Setting Hanging Indent
From your Google Doc page, 1) click on the font type icon and 2) select your font type from the drop down list. For both MLA and APA you will select Times New Roman.
Next, you need to set the font size to 12 for both MLA and APA. 1) Click on the font size icon and 2) select 12.
From your Google Doc, 1) click on File from the menu and 2) select Page Setup from the drop down menu.
Next you can set the margins as you need them to be and click OK. MLA and APA both use 1 inch margins all around which is the Google Docs default.
From your Google Doc, 1) click on the Line Spacing icon (looks like lines with an arrow up and down) and 2) select Double from the drop down menu.
From your Google Doc, 1) click on Format from the menu, 2) put your cursor on Line Spacing and 3) select Double from the pop out menu.
From your Google Doc
1) click on the Insert menu
2) move your cursor to the Header & page number selection
3) move your cursor to the Page number selection and select the page number setup you want (usually the page number top right as show in the image below)
Create a Hanging Indent Using Google Docs
- After you have typed and centered Works Cited at the top of the page, press the Enter key.
- Select Hanging from the Special Indent dropdown menu
- Start typing your citation. When you reach the end of the link the program will indent the second line automatically. When you finish your citation and press the Enter key, the program will start the next citation at your left margin. Keep entering your citations in alphabetical order by first element and you're done!
- << Previous: Formatting Basics -- Word
- Next: Set Default Formatting in Word & Google Docs >>
- Last Updated: Nov 12, 2020 6:20 PM
- URL: https://gavilan.libguides.com/researchpaperformatting
How-To Geek
How to find and add citations in google docs.
Your changes have been saved
Email is sent
Email has already been sent
Please verify your email address.
You’ve reached your account maximum for followed topics.
Today's NYT Connections Hints and Answer for August 14 (#429)
Spectrum just raised its prices again, why it feels like ads are listening to your conversations (when they aren't).
When writing papers, you need to generate a detailed and accurate list of all the sources you've cited in your paper. With Google Docs, you can easily find and then add citations to all of your research papers.
Fire up your browser, head over to Google Docs, and open up a document. At the bottom of the right side, click the "Explore" icon to open up a panel on the right.
Alternatively, press Ctrl+Alt+Shift+I on Windows/Chrome OS or Cmd+Option+Shift+I on macOS to open it using the keyboard shortcut.
Related: All of the Best Google Docs Keyboard Shortcuts
Explore is kind of like the Google Assistant of Docs. When you open the tool, it parses your document for related topics to speed up web searches and images you can add in Docs.
If Explore isn't able to find anything relatable in your document, type what you're looking for in the search bar and hit the "Enter" key to search the web manually.
Click the three vertical dots and choose what style of citation you want to use. The options are MLA, APA, and Chicago styles.
Next, highlight the text---or place the text cursor--- where you want to add a citation to, hover over the search result in the Explore panel, and then click the "Cite as footnote" icon that appears.
After you click the icon, Docs will number the citation and cite the link in a footnote of the page.
You can add as many as you need for your document. Redo the search and click the "Cite as footnote" icon beside each result to have Docs automatically compile citations for you.
- Google Docs
- Cloud & Internet
Kernel Panic
Data visualization and analysis, mapping, and other tech tools
Research Tools for Google Docs
Google Docs ( google.com/docs/about ) has evolved to the point where it is a viable alternative to desktop word processors (see 10 Reasons to Love Google Docs and 6 Tips for Writing Your Thesis in Google Docs ). It works completely in the cloud , so that you can access your documents from anywhere, on any device, even without an Internet connection . Documents are easily shared and editable by multiple people simultaneously. In addition, an increasing number of add-on tools make Google Docs an even more powerful solution for writing research papers. This post discusses some of the tools and add-ons that will help you write your research paper using Google Docs.
See also the Introduction to Google Drive and Google Apps .
The Google Docs Research Tool
While editing your paper in Google Docs, click the Tools menu, then Research:
This will bring up a Research panel along the right-hand edge of your browser window:
The Research Tool lets you conduct internet research and incorporate the results into your document, without having to leave Google Docs.
If we were writing a paper about environmental pollution, for example, we could search the web by choosing Everything from the drop-down menu. Typing in the name of a book, say Silent Spring , brings up some information on that publication, as well as links to relevant web pages.
You can also highlight some text in your document, and then click on Tools|Research, and your search will be done with the selected text as the search criteria.
This can also be accomplished by right-clicking on highlighted text, and then choosing Research from the popup menu. You also have the option to define the highlighted text:
If you use information from a web source in your paper, you can cite the source by hovering your mouse cursor over the source and clicking Cite:
This will insert a numbered superscript into your text at the current cursor location, and the corresponding footnote at the bottom of the page:
If instead of Everything, you search for Images, your search results will consist of a series of images that are relevant to your search terms. You can filter the search so that you only retrieve images that are free to use:
If you hover your mouse cursor over an image in the results panel, you will see the internet source of the image. Dragging an image from the results panel onto your document will insert the image along with a footnote that documents the source:
You can also search the internet for quotes. For example, to search for quotes by Thomas Edison, enter his name into the Research Tool, and then choose to search for Quotes:
If you hover your mouse cursor over a quote in the results panel, the source of the quote will be revealed, along with the option to find other sources of the same quote. You can insert a quote into your document by hovering over the quote and clicking the Insert button. The quote will be inserted along with an appropriate footnote to indicate the source:
The Research Tool also allows you to search Google Scholar . Rather than searching the entire web, Google Scholar searches academic publishers, universities, and other sources for peer-reviewed articles, academic theses and books, technical reports, and other scholarly publications. It is important to understand that Google Scholar is not a bibliographic database like those found in college and university libraries. Such databases are subscription-based resources that search for peer-reviewed articles that have been published in academic journals. Bibliographic databases also contain more recent articles than can be found in Google Scholar and have more powerful search functionality than Google Scholar.
Nevertheless, Google Scholar can be a useful tool when writing a research paper in Google Docs. In the Research Tool, choose Google Scholar from the drop-down menu and type in a search term:
The result is a list of scholarly articles. By hovering your mouse cursor over an article, you can either insert the complete citation or insert a numbered superscript at the current cursor location with a corresponding footnote. The Research Tool lets you choose from three citation styles for footnotes: MLA (Modern Language Association), APA (American Psychological Association), and Chicago (University of Chicago Manual of Style).
Lastly, the Research Tool allows you to search for, cite, and import tables of data found on the web. From the Research Tool dropdown menu, choose Tables, and then enter a search term:
The Research Tool will find tables of data that match the search criteria. Clicking the link for a table will bring you to the website that contains the table. When you hover your mouse cursor over a table in the Table Search results panel, you will have the option of either citing the table or exporting the table’s data to a spreadsheet. Citing the table inserts a numbered superscript into your text at the current cursor location and a corresponding footnote containing the source of the table. Clicking Export lets you either export the table to a Google Sheet (spreadsheet) or to a Google Fusion Table (a more powerful type of spreadsheet with additional data managment and visualization tools). Most of the time you either want to paste the table into your document or create a graphic based on the table data to use in your document. Exporting the table to a Google Sheet will let you accomplish both tasks.
When you successfully export the table, the result will look similar to this:
When you click the link to open the spreadsheet, you will be shown the Google Sheet containing the table data:
From the spreadsheet you can copy and paste data into your Google document. . . .
. . . . and it will be formatted as a table:
From the Google Sheet you can also process the data further, perhaps to generate a graphic for your paper:
In this example the “Annual US Cost” column did not originally contain numerical values, so that we had to create a new column called “Cost (billions)” to contain the numeric cost values in billions of dollars. We then could use the new data column to produce a bar chart to insert into our research paper.
For web searching and citing sources, the Google Docs Research Tool is fine as far as it goes. However, it does have its limitations. It does not handle multiple citations well. The Research Tool also will not automatically reformat the in-text citations and bibliography of your paper if you decide to use a different citation style. It is also severely limited in the number of available citation styles. In fact, the Research Tool doesn’t even have different styles for in-text citations: all it does is footnotes. For the bibliography itself, the Google Docs Research Tool only has the MLA, APA, and Chicago styles.
To obtain some research-grade academic citation features, we need a more powerful reference management system. We can gain access to such a tool through the use of add-ons to Google Docs.
Google Docs Add-ons
One of the most powerful features of Google Docs is the ability to install add-on tools . We will examine ProQuest RefWorks , a reference management system provided by many colleges and universities, to show how to find, install, and use add-ons for Google Docs.
In Google Docs, click the Add-ons menu:
The Add-ons menu will show any add-ons you have installed. To install more add-ons, click Get add-ons… and you will be shown a searchable list of available third-party add-ons.
For this example, we will enter RefWorks in the search bar:
. . . which should find the entry for ProQuest RefWorks. Click the button to install the ProQuest RefWorks add-on to Google Docs. After granting the necessary permissions, you should now have an entry for ProQuest RefWorks in your Add-ons menu:
Next, click Manage citations , which will bring up the ProQuest RefWorks panel at the right-hand side of your browser window:
You need to log in with your RefWorks user name (email address) and password, which you set up when you opened your ProQuest RefWorks account. If your institution subscribes to ProQuest RefWorks, you can open an account by visiting refworks.proquest.com and clicking Create account .
A Digression At this point, you may be asking: “What account?” ProQuest RefWorks (“the New RefWorks”) is a web-based reference manager, which means it runs inside of your web browser. In order to use ProQuest RefWorks, you need to have an account, and in order to have an account, your academic institution has to have a ProQuest RefWorks subscription. ProQuest RefWorks is called the “New RefWorks” because there is an “Old RefWorks.” ProQuest RefWorks lives at refworks.proquest.com and looks like this: Old RefWorks lives at www.refworks.com and looks like this: Because there are a lot of Old RefWorks users (what I meant to say was “users of Old RefWorks”), ProQuest is keeping Old RefWorks around for a while. Once you have set up an account in ProQuest RefWorks, you can import your references that are stored in the Old RefWorks as follows. First, log in to your ProQuest RefWorks account. You will be brought to your home page, which will look something like this: Click the plus sign to bring up the Add a reference dialog and then choose Import references : Under Import from another reference manager , choose RefWorks: You will then be asked to authorize the import, and then you will be brought to the Old RefWorks login screen. Log in with your Old RefWorks credentials: If the import is successful, you should then be returned to your ProQuest RefWorks home page with a list of the imported references: Once you have some references in your ProQuest RefWorks account, they will show up in the Google Docs ProQuest RefWorks add-on when you sign in with your ProQuest RefWorks user name and password. You cannot add or delete references from the Google Docs add-on; you add and delete references from the ProQuest RefWorks web interface and then access that database from the add-on. Because this post is about the Google Docs ProQuest RefWorks add-on, we will not go into detail about use of the ProQuest RefWorks web interface. However, you can find more information about the ProQuest RefWorks web interface at Welcome to the New RefWorks . One final point. In order to use the Google Docs ProQuest RefWorks add-on, you must use the Google Chrome web browser. While you can use Google Docs in other browsers, the various add-on tools only work in Chrome.
The ProQuest RefWorks Add-on
Back in Google Docs, and assuming that you have successfully logged in to your ProQuest RefWorks account, you should now see your list of references in the ProQuest RefWorks add-on:
The RefWorks add-on for Google Docs (I am now tired of typing “ProQuest RefWorks”) basically works like the Google Docs Research Tool, only better.
First, click the gear icon in the RefWorks panel to open the Settings dialog:
In the small popup menu, choose Change citation style :
A window will appear showing the current citation style. If you click in the search bar, you will see a list of recently-used styles. Choose one of these styles, and then click the Update button to set that style as the current citation style for the document. In this example we have chosen the Chicago Manual of Style 16th Edition, which is an author-date citation style.
Now, as you are writing your research paper, place your cursor in the text where you want to insert a citation. If you then hover your mouse cursor over a reference in the RefWorks panel, you will have the option of either citing the reference, or first editing and then citing.
Choose Cite this . RefWorks will place an in-text citation at the cursor location and immediately start creating a formatted bibliography in your chosen style:
If you instead choose Edit and Cite , you can modify how the citation will appear before you insert it into the text:
For example, you can suppress display of the author, so that your citation might now look like:
You can search for particular references by using the search bar in the RefWorks panel:
To cite multiple references at once, leave your cursor in place in the text while you click in the RefWorks panel to cite multiple references in succession. The references will be inserted at the same place in the text, and RefWorks will dynamically update the bibliography:
You can change the citation style of your paper by going back to the RefWorks add-on Settings menu. Choose Change citation style . You can choose from hundreds of citation styles (instead of just the 3 that the Google Docs Research Tool offers) by using the search bar. If for example you start typing “Journal of” in the search bar you will see that there are over 1200 styles that begin with “Journal of.” Search for and choose the style for the journal Nature , then click Update. The in-text citations and the bibliography will be immediately re-formatted to reflect the new style:
This is a very different bibliographic style from Chicago, because numbered superscripts are used in the text instead of the Chicago style’s author-date system. Also, in the bibliography, references are listed in citation order instead of being listed alphabetically by author. The RefWorks add-on can easily switch between citation styles and does a nice job of correctly formatting superscripts and author-date pairs in the text to cite multiple references.
Once your paper is finished, you have the option of downloading it in several common document formats. In Google Docs, click the File menu, then hover over Download as . You will see several download options:
Your in-text citations and formatted bibliography will be preserved in the downloaded file.
You can share your Google document with collaborators by using the Share button. This lets you enter a list of names or email addresses for sharing the document. Share your Google Doc with anyone who has a RefWorks account and in addition to adding and editing text to your document, they can also add in-text citations and footnotes from their RefWorks account. You can even collaborate using the same set of references by sharing your RefWorks collection with your collaborators via the ProQuest RefWorks web interface. Anyone you share with can also delete in-text citations you’ve included in your document. However, they cannot delete references from your RefWorks account.
For more information on sharing with RefWorks see New RefWorks: Sharing and Collaborating .
There are also some free add-on reference managers for Google Docs, such as EasyBib and Paperpile . These can be found by searching for “citation managers” in the Get add-ons menu option:
While the EasyBib and Paperpile add-ons are free, they usually also offer the option to purchase a subscription. The subscription buys you more features, such as the ability to import references from an external source. While not as powerful as a full-featured, commercial reference manager such as RefWorks, these free tools do allow you to search for references and create formatted bibliographies from inside of Google Docs.
So Many Papers, so Little Time
A blog about scientific publishing and academic productivity
- Paperpile News
- Productivity
- Inspiration
Google Docs ♥ Paperpile
It’s been nearly two years since we released the first public version of Paperpile, a reference manager built from scratch for the web. During this time, Paperpile has grown into a fully featured tool used by thousands of researchers every day to find, collect, manage, read, annotate, share and write papers, boosting their academic productivity.
Today, we’re delighted to announce the release of a free fully featured citation manager as a standalone product that works with Google Docs, enabling you to collaboratively write papers and grants. Now everyone can add citations and bibliographies to a Google Doc, no account or sign-up is required.
Add our citation app in one click from the Google Docs add-on store !
Writing a paper in Google Docs the Paperpile way works like this:
- Install the Google Docs add-on
- Invite your colleagues to your documents and ask them to install the add-on.
- Add citations, here’s our cheat sheet
Collaborative writing needs collaborative citing
Many of us have observed that the author lists of academic papers are getting longer and longer. This is not surprising as science gets more interdisciplinary and collaborations grow. There has been some controversy surrounding the trend of “hyper authorship” with questions about the relative contributions of every author and the meaning of “authorship”.
However, we’re more interested in the practical aspects of this debate. How do you write a paper with hundreds of authors in the first place? Even if only 10% of 200 authors of a paper actually take part in the writing there’s the not insignificant we are faced with the problem of 20 people editing the same document.
With a background in genomics, I’ve personally been part of several big consortia. I remember only too well editing the manuscript and supplementary text on a weekend right before the deadline for the paper submission of the ModENCODE project . I was not alone, more than 10 others were writing at the same time in a Google Doc.
That was back in 2011 and by then Google Docs already had impressively solved the problem of collaborative writing. Unfortunately this was not the case for academics. Citations and references were, frankly, a mess.
At some point in this painful process, the document was exported to Word because some poor colleague had to use EndNote to compile all the references. While the writing was a truly collaborative experience, reference management was a one-person-job and team productivity was set back by many years!
The manuscript was then sent around via email, it could only be edited by one person at a time and it required many “manuscript_final3.docx” versions until final submission. And of course, for the revisions we re-imported to Google Docs and started this tedious process all over again…
Our solution
Paperpile is about academic productivity. Fixing collaborative writing is a big part of this equation. As a startup, we occupy this space with others like ShareLatex , Overleaf , and Authorea which provide authoring platforms for academics.
Our approach is different. Paperpile does not seek to replace Google Docs but rather to extend it and make it a first class platform for academic writing.
A free, fully-featured citation manager for Google Docs
What if there was a citation manager that came with multiple benefits and was as easy to use as Google Docs itself? Today we are launching our free Paperpile add-on for Google Docs, which we think comes very close:
- Free, can be added with one click from the Google Docs add-on store .
- No subscription or even sign-up required. Just use your Google account.
- Self-contained and fully-featured. Includes everything that’s required to prepare citations and bibliography for an academic paper.
- Fully collaborative, everyone can add, view and edit citations at the same time.
What can you do with the new add-on? The best way to find out is to try it out for yourself but here is a rundown of features:
- Search online for journal articles and books while writing the paper
- Look up PubMed IDs, DOIs and ISBNs
- Add citations and bibliographies with one click
- Supports all types of citations (in-text, footnotes, author-name, numeric, superscripts,…)
- Choose among all major citation styles like APA, Chicago, MLA and thousands of journal-specific styles
- Citations are customizable so that they fit in the sentence without getting lost when you re-format, e.g. “(see also Smith, 2002 and references therein)
- Supports italics and superscripts in the bibliography (e.g. for species names and chemical compounds)
- Cites journals and books and 29 other supported reference types including patents, websites, computer programs, data sets,..
- Export references as RIS or BibTeX for use with other reference managers (we don’t judge…)
- Export your document to Word and EndNote
- Export your document with citation commands suitable for use with LaTeX/BibTeX
- Get support at forum.paperpile.com
Writing my next paper in Google Docs, really?
Is this a good idea? Yes and we have the data to prove it!
Writing is part of every academic’s job. Actually it’s a mission critical part of it. Your career depends on the papers you publish, your financing depends on the grants you write and as a student your degree depends on your thesis.
We understand that there is some activation energy required to switch from a well-established workflow like Word and EndNote to something completely different like Google Docs and Paperpile.
That’s why we are very happy to see that so many of Paperpile customers actually made this switch. We did a survey and here are some statistics from a subset of paying customers.
If we extrapolate these results out, there are thousands of papers already published that were written with Paperpile and Google Docs.
Success stories
We’ve improved Paperpile constantly over the past two years (e.g. here and here ), mainly guided by user feedback. Along the way we’ve had more than 2,000 individual conversations with our users and heard many of their success stories.
Les Ansley reported how Paperpile helped him to write a collaborative paper across five European countries and eight institutions . He is Senior lecturer at Northumbria University and the paper is about Pathophysiological mechanisms of exercise-induced anaphylaxis .
Brady Allred, Professor of Rangeland Ecology at the University of Montana, used Google Docs and Paperpile to write his paper about “ Ecosystem services lost to oil and gas in North America ” published in Science this April.
Carlos Araya is a Postdoc at the Dept. of Genetics, Stanford University. His paper “ Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes ” was featured on the cover of Nature Biotechnology . This paper was entirely written in Google Docs and Paperpile.
Mara Lawniczak studies malaria as group leader at the Wellcome Trust Sanger Institute. She used Google Docs and Paperpile for a recent review . What did we learn from this review (except how genotypes are connected to medically relevant phenotypes in major vector mosquitoes of course…)? That there are still papers with just one author and also for this use case Paperpile is the best solution. She sent us a message: “ Being able to say good bye forever to EndNote/Word is delightful . And the bells and whistles of Paperpile, yet its simplicity in use, make it sooooo nice.”
We’re happy to see how Paperpile helps so many people across different fields, countries and career stages. We could go on. There’s Nils Loewen Professor in the Department of Ophthalmology at the University of Pittsburgh who used Paperpile and Google Docs to write a successful $1M NIH grant or Thomas Secher and undergrad student who wrote his master’s thesis in marketing at Stockholm Business School in Google Docs…
What’s new for existing Paperpile subscribers?
Our Chrome extension allowed Paperpile subscribers to add citations to their Google Docs since day one, this is how it looks . Nothing will change about our Chrome extension and how it integrates with Google Docs. We will keep improving it because it offers crucial features like the very powerful popup citation dialog or keyboard shortcuts.
The new sidebar add-on announced today can be safely used in parallel and adds several new features like the frequently requested EndNote export . The side-bar view also gives you a better overview of existing citations and makes it easier to add multiple citations from a single search result (another popular feature request).
Most importantly, however, the new add-on allows all your co-authors to fully participate in the citation process even if they have not switched (yet) to Paperpile as their main reference manager. The sidebar add-on is completely self-contained and does not require a paid subscription. Paid subscriptions remain unchanged and offer all advanced features you expect from a modern reference manager.
Brought to you by the folks at Paperpile.
We love papers so we blog about it.
How to Write Google Docs Citations: A Step-by-Step Guide
Citing sources in Google Docs is an essential skill for students and professionals alike. It gives credit to the original authors, avoids plagiarism, and adds credibility to your work. Follow this quick guide, and you’ll be citing like a pro in no time.
Step by Step Tutorial: How to Write Google Docs Citations
Google Docs makes it easy to include citations in your documents. This section will take you through the steps to add citations correctly.
Step 1: Open the ‘Tools’ Menu
Click on the ‘Tools’ menu at the top of your Google Doc.
In the ‘Tools’ menu, you’ll find various options to enhance your document. For citations, you’ll be using the ‘Citations’ feature.
Step 2: Select ‘Citations’
Choose ‘Citations’ from the dropdown menu.
The ‘Citations’ tool is specifically designed to add and format citations in your document. It supports various citation styles, such as MLA, APA, and Chicago.
Step 3: Choose a Citation Style
Pick a citation style (MLA, APA, or Chicago) from the sidebar that appears.
Each citation style has different formatting rules. Your teacher or publisher will usually tell you which style to use.
Step 4: Add Citation Source Information
Click on ‘+ Add citation source’ and fill in the details of your source.
Be thorough when entering your source information. The more accurate your details, the more reliable your citations will be.
Step 5: Insert the Citation
After adding your source, click on the ‘Cite’ button to insert the citation into your document.
Your citation will now appear in the proper format, according to the style you selected. You can edit or delete it if needed.
Once you’ve completed these steps, your document will have properly formatted citations. This adds a professional touch to your work and ensures that you’re giving appropriate credit to the sources you’ve used.
Tips for Writing Google Docs Citations
Here are some helpful tips to ensure your citations are spot-on:
- Double-check the spelling of authors’ names and titles; accuracy is key.
- If you use a direct quote, include the page number in your citation.
- Keep your citations consistent; stick to one style throughout your document.
- Use the citation tools built into Google Docs for convenience and accuracy.
- Regularly save your document to avoid losing any citation information.
Frequently Asked Questions
What if i can’t find all the information for a source.
Try to find as much information as possible, but if some details are missing, include what you have. Consistency and accuracy are important, but incomplete citations are better than none.
Can I cite sources in footnotes using Google Docs?
Yes, you can. In addition to the ‘Citations’ tool, Google Docs offers a feature to insert footnotes. You can manually add citation details there.
How do I cite a website without an author?
In such cases, start with the title of the article or webpage. Follow the rest of the citation format for the style you are using.
Can I change the citation style after adding citations?
Yes, you can change the style even after you’ve added citations. Google Docs will automatically update your citations to the new format.
How do I remove a citation?
Click on the citation in your document, then choose the ‘Edit’ or ‘Delete’ option from the citation tool sidebar.
Here’s a quick recap of the steps:
- Open the ‘Tools’ menu.
- Select ‘Citations’.
- Choose a citation style.
- Add citation source information.
- Insert the citation.
Mastering how to write Google Docs citations is a breeze once you get the hang of it. With the built-in citation tool, Google Docs does most of the heavy lifting for you. Remember that citing sources is not just a formality; it’s about joining a larger conversation within your field or subject area. Plus, it’s the ethical thing to do. So next time you’re wrapping up that research paper or article, give the citation tool a spin. It might just become your new best friend in the writing process.
Matthew Burleigh has been writing tech tutorials since 2008. His writing has appeared on dozens of different websites and been read over 50 million times.
After receiving his Bachelor’s and Master’s degrees in Computer Science he spent several years working in IT management for small businesses. However, he now works full time writing content online and creating websites.
His main writing topics include iPhones, Microsoft Office, Google Apps, Android, and Photoshop, but he has also written about many other tech topics as well.
Read his full bio here.
Share this:
Join our free newsletter.
Featured guides and deals
You may opt out at any time. Read our Privacy Policy
Related posts:
- How to Do a Hanging Indent on Google Docs
- How to Edit Citations in Google Docs: A Step-by-Step Guide
- How to Delete a Google Docs Citation: A Step-by-Step Guide
- How to Insert Text Box in Google Docs
- How to Subscript in Google Docs (An Easy 4 Step Guide)
- How to Delete a Table in Google Docs (A Quick 5 Step Guide)
- How to Insert a Horizontal Line in Google Docs
- How to Center a Table in Google Docs (2023 Guide)
- How to Double Space on Google Docs – iPad, iPhone, and Desktop
- How to Cite Images in PowerPoint: A Step-by-Step Guide
- How to Cite Images in a PowerPoint: APA Style Guide
- How to Remove Strikethrough in Google Docs (A Simple 4 Step Guide)
- How to Add a Row to a Table in Google Docs
- How to Create a Folder in Google Docs
- Google Docs Space After Paragraph – How to Add or Remove
- Can I Convert a PDF to a Google Doc? (An Easy 5 Step Guide)
- How to Clear Formatting in Google Docs
- How to Make Google Docs Landscape
- How to Delete A Google Doc (An Easy 3 Step Guide)
- How to Insert a Page Break in Google Docs
Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
- Algorithms and Optimization 323
- Applied science 186
- Climate and Sustainability 10
- Cloud AI 46
- Euphonia 12
- Language 235
- Perception 291
Research Area
- Algorithms and Theory 1984
- Climate and Sustainability 16
- Data Management 333
- Data Mining and Modeling 479
- Distributed Systems and Parallel Computing 514
- Economics and Electronic Commerce 420
- Education Innovation 86
- General Science 466
- Hardware and Architecture 217
- Health & Bioscience 444
- Human-Computer Interaction and Visualization 1114
- Information Retrieval and the Web 694
- Machine Intelligence 4535
- Machine Perception 1832
- Machine Translation 198
- Mobile Systems 183
- Natural Language Processing 1356
- Networking 490
- Quantum Computing 139
- Responsible AI 228
- Robotics 339
- Security, Privacy and Abuse Prevention 846
- Software Engineering 272
- Software Systems 692
- Speech Processing 688
- Title, descending
- Year, descending
Learn more about how we conduct our research
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work.
Sign up for our daily newsletter
- Privacy Policy
- Advertise with Us
How to Research and Cite Articles in Google Docs
When you’re writing a paper it can be frustrating to get your citations sorted. Knowing what to cite, when, and in what style can add more undue stress to writing a paper. Thankfully, over the years, many online services have been created that automate the citation process. Google Docs takes it a step further, however, by allowing you to do your research and citing all within the document itself! This makes for an easier time finding and citing sources that relate to what you’re trying to say.
Opening the Explore Panel
At one stage in Google Doc’s life, it had a panel called “Research” that allowed you to do just that. These days it has the somewhat confusing name “Explore” but still fills the niche of doing research within Google Docs. To open the Explore panel, click on “Tools” at the top, then “Explore.”
An Explore panel will open on the right side.
Performing Research
Now that the Explore panel is open, you can use it to perform research. The easiest way to think about Explore is like a personal Google you can have open on the side of your document as you’re writing. As such, whenever you need to look something up, you can type it into the Explore panel as you would into Google. Explore will show you a list of results.
Click on the one you like the look of the most, and it will appear in a new tab for you to read. If you already have a website in mind that you’d like to cite, you can simply enter the URL into the search box and Explore will do the rest.
If you notice, along the top is the option for images. By clicking on each image, you can see a larger version of it, as well as details on its usage license. Clicking “Insert” adds the image to your document. Make sure it has the correct license, and always cite work if needed!
Citing Research
Once you’ve found a website that you’d like to cite, write about it in your paper. Then, put the blinking cursor at the point in the document where you would like to add a citation.
Hover over the result you’d like to cite here. Click the quotation button will appear to the top right of the result.
Once clicked, two things will happen. First, Google Docs will add a superscript number where your cursor is to identify it. Then, Google will cite it at the bottom of the document. It’s worth noting that this citation isn’t written into the footer, so you can still use it for page numbers and other options.
Changing Citation Format
Institutes often enforce specific citation formats. If you want to change the formatting of the citations, click the three dots beside the search box. Here you can choose between MLA, APA, and Chicago formats.
Making a Bibliography
For papers that require a bibliography, Explore may not be ideal. Instead of having the citations at the bottom of the page, bibliographies are typically put at the very end of the paper. If your institute requires a bibliography instead, there’s two ways you can get around this.
Cut and Paste the Citations
There’s no way to set Explore to make a bibliography automatically, so you’ll have to make one yourself. Cite sources as you would above, then shift all the footnotes it creates into a bibliography section. Make sure the citation style matches what is required for the bibliography.
Use an Addon
Alternatively, Google Docs has some nice addons which automate the process for you and generate a bibliography on the fly. One such addon is EasyBib Bibliography Creator . It can be installed into Docs and used to create a full bibliography. Once installed, access it using the “Addons” menu at the top of Google Docs.
In My Cites
Arranging and formatting citations can be a major time sink. By using Google Doc’s explore feature, you can automate an otherwise monotonous part of writing a paper. If it’s not up to your standards, there are addons to help get the perfect citations.
Do you find citing papers a chore? Let us know below!
Our latest tutorials delivered straight to your inbox
Simon Batt is a Computer Science graduate with a passion for cybersecurity.
Free Download
Research Paper Template
The fastest (and smartest) way to craft a research paper that showcases your project and earns you marks.
Available in Google Doc, Word & PDF format 4.9 star rating, 5000 + downloads
Step-by-step instructions
Tried & tested academic format
Fill-in-the-blanks simplicity
Pro tips, tricks and resources
What It Covers
This template’s structure is based on the tried and trusted best-practice format for academic research papers. Its structure reflects the overall research process, ensuring your paper has a smooth, logical flow from chapter to chapter. Here’s what’s included:
- The title page/cover page
- Abstract (or executive summary)
- Section 1: Introduction
- Section 2: Literature review
- Section 3: Methodology
- Section 4: Findings /results
- Section 5: Discussion
- Section 6: Conclusion
- Reference list
Each section is explained in plain, straightforward language , followed by an overview of the key elements that you need to cover within each section.
You can download a fully editable MS Word File (DOCX format), copy it to your Google Drive or paste the content to any other word processor.
download your copy
100% Free to use. Instant access.
I agree to receive the free template and other useful resources.
Download Now (Instant Access)
FAQs: Research Paper Template
What format is the template (doc, pdf, ppt, etc.).
The research paper template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.
What types of research papers can this template be used for?
The template follows the standard best-practice structure for formal academic research papers, so it is suitable for the vast majority of degrees, particularly those within the sciences.
Some universities may have some additional requirements, but these are typically minor, with the core structure remaining the same. Therefore, it’s always a good idea to double-check your university’s requirements before you finalise your structure.
Is this template for an undergrad, Masters or PhD-level research paper?
This template can be used for a research paper at any level of study. It may be slight overkill for an undergraduate-level study, but it certainly won’t be missing anything.
How long should my research paper be?
This depends entirely on your university’s specific requirements, so it’s best to check with them. We include generic word count ranges for each section within the template, but these are purely indicative.
What about the research proposal?
If you’re still working on your research proposal, we’ve got a template for that here .
We’ve also got loads of proposal-related guides and videos over on the Grad Coach blog .
How do I write a literature review?
We have a wealth of free resources on the Grad Coach Blog that unpack how to write a literature review from scratch. You can check out the literature review section of the blog here.
How do I create a research methodology?
We have a wealth of free resources on the Grad Coach Blog that unpack research methodology, both qualitative and quantitative. You can check out the methodology section of the blog here.
Can I share this research paper template with my friends/colleagues?
Yes, you’re welcome to share this template. If you want to post about it on your blog or social media, all we ask is that you reference this page as your source.
Can Grad Coach help me with my research paper?
Within the template, you’ll find plain-language explanations of each section, which should give you a fair amount of guidance. However, you’re also welcome to consider our private coaching services .
Additional Resources
If you’re working on a research paper or report, be sure to also check these resources out…
1-On-1 Private Coaching
The Grad Coach Resource Center
The Grad Coach YouTube Channel
The Grad Coach Podcast
Free Al Office Suite with PDF Editor
Edit Word, Excel, and PPT for FREE.
Read, edit, and convert PDFs with the powerful PDF toolkit.
Microsoft-like interface, easy to use.
Windows • MacOS • Linux • iOS • Android
- Articles of Word
How to Write a Research Paper [Steps & Examples]
As a student, you are often required to complete numerous academic tasks, which can demand a lot of extra effort. Writing a research paper is one of these tasks. If researching for the topic isn't challenging enough, writing it down in a specific format adds another layer of difficulty. Having gone through this myself, I want to help you have a smoother journey in writing your research paper. I'll guide you through everything you need to know about writing a research paper, including how to write a research paper and all the necessary factors you need to consider while writing one.
Order for Preparation of your research paper
Before beginning your research paper, start planning how you will organize your paper. Follow the specific order I have laid out to ensure you assemble everything correctly, cover all necessary components, and write more effectively. This method will help you avoid missing important elements and improve the overall quality of your paper.
Figures and Tables
Assemble all necessary visual aids to support your data and findings. Ensure they are labeled correctly and referenced appropriately in your text.
Detail the procedures and techniques used in your research. This section should be thorough enough to allow others to replicate your study.
Summarize the findings of your research without interpretation. Use figures and tables to illustrate your data clearly.
Interpret the results, discussing their implications and how they relate to your research question. Address any limitations and suggest areas for future research.
Summarize the key points of your research, restating the significance of your findings and their broader impact.
Introduction
Introduce the topic, provide background information, and state the research problem or hypothesis. Explain the purpose and scope of your study.
Write a concise summary of your research, including the objective, methods, results, and conclusion. Keep it brief and to the point.
Create a clear and informative title that accurately reflects the content and focus of your research paper.
Identify key terms related to your research that will help others find your paper in searches.
Acknowledgements
Thank those who contributed to your research, including funding sources, advisors, and any other significant supporters.
Compile a complete list of all sources cited in your paper, formatted according to the required citation style. Ensure every reference is accurate and complete.
Types of Research Papers
There are multiple types of research papers, each with distinct characteristics, purposes, and structures. Knowing which type of research paper is required for your assignment is crucial, as each demands different preparation and writing strategies. Here, we will delve into three prominent types: argumentative, analytical, and compare and contrast papers. We will discuss their characteristics, suitability, and provide detailed examples to illustrate their application.
A.Argumentative Papers
Characteristics:
An argumentative or persuasive paper is designed to present a balanced view of a controversial issue, but ultimately aims to persuade the reader to adopt the writer's perspective. The key characteristics of this type of paper include:
Purpose: The primary goal is to convince the reader to support a particular stance on an issue. This is achieved by presenting arguments, evidence, and refuting opposing viewpoints.
Structure: Typically structured into an introduction, a presentation of both sides of the issue, a refutation of the opposing arguments, and a conclusion that reinforces the writer’s position.
Tone: While the tone should be logical and factual, it should not be overly emotional. Arguments must be supported with solid evidence, such as statistics, expert opinions, and factual data.
Suitability:
Argumentative papers are suitable for topics that have clear, opposing viewpoints. They are often used in debates, policy discussions, and essays aimed at influencing public opinion or academic discourse.
Topic: "Should governments implement universal basic income?"
Pro Side: Universal basic income provides financial security, reduces poverty, and can lead to a more equitable society.
Con Side: It could discourage work, lead to higher government expenditure, and might not be a sustainable long-term solution.
Argument: After presenting both sides, the paper would argue that the benefits of reducing poverty and financial insecurity outweigh the potential drawbacks, using evidence from various studies and real-world examples.
Writing Tips:
Clearly articulate your position on the issue from the beginning.
Present balanced arguments by including credible sources that support both sides.
Refute counterarguments effectively with logical reasoning and evidence.
Maintain a factual and logical tone, avoiding excessive emotional appeals.
B.Analytical Papers
An analytical research paper is focused on breaking down a topic into its core components, examining various perspectives, and drawing conclusions based on this analysis. The main characteristics include:
Purpose: To pose a research question, collect data from various sources, analyze different viewpoints, and synthesize the information to arrive at a personal conclusion.
Structure: Includes an introduction with a clear research question, a literature review that summarizes existing research, a detailed analysis, and a conclusion that summarizes findings.
Tone: Objective and neutral, avoiding personal bias or opinion. The focus is on data and logical analysis.
Analytical research papers are ideal for topics that require detailed examination and evaluation of various aspects. They are common in disciplines such as social sciences, humanities, and natural sciences, where deep analysis of existing research is crucial.
Topic: "The impact of social media on mental health."
Research Question: How does social media usage affect mental well-being among teenagers?
Analysis: Examine studies that show both positive (e.g., social support) and negative (e.g., anxiety and depression) impacts of social media. Analyze the methodologies and findings of these studies.
Conclusion: Based on the analysis, conclude whether the overall impact is more beneficial or harmful, remaining neutral and presenting evidence without personal bias.
Maintain an objective and neutral tone throughout the paper.
Synthesize information from multiple sources, ensuring a comprehensive analysis.
Develop a clear thesis based on the findings from your analysis.
Avoid inserting personal opinions or biases.
C.Compare and Contrast Papers
Compare and contrast papers are used to analyze the similarities and differences between two or more subjects. The key characteristics include:
Purpose: To identify and examine the similarities and differences between two or more subjects, providing a comprehensive understanding of their relationship.
Structure: Can be organized in two ways:
Point-by-Point: Each paragraph covers a specific point of comparison or contrast.
Subject-by-Subject: Each subject is discussed separately, followed by a comparison or contrast.
Tone: Informative and balanced, aiming to provide a thorough and unbiased comparison.
Compare and contrast papers are suitable for topics where it is important to understand the distinctions and similarities between elements. They are commonly used in literature, history, and various comparative studies.
Topic: "Compare and contrast the leadership styles of Martin Luther King Jr. and Malcolm X."
Comparison Points: Philosophies (non-violence vs. militant activism), methods (peaceful protests vs. more radical approaches), and impacts on the Civil Rights Movement.
Analysis: Describe each leader's philosophy and method, then analyze how these influenced their effectiveness and legacy.
Conclusion: Summarize the key similarities and differences, and discuss how both leaders contributed uniquely to the movement.
Provide equal and balanced coverage to each subject.
Use clear criteria for comparison, ensuring logical and coherent analysis.
Highlight both similarities and differences, ensuring a nuanced understanding of the subjects.
Maintain an informative tone, focusing on objective analysis rather than personal preference.
How to Write A Research Paper [Higher Efficiency & Better Results]
Conduct Preliminary Research
Before we get started with the research, it's important to gather relevant information related to it. This process, also known as the primary research method, helps researchers gain preliminary knowledge about the topic and identify research gaps. Whenever I begin researching a topic, I usually utilize Google and Google Scholar. Another excellent resource for conducting primary research is campus libraries, as they provide a wealth of great articles that can assist with your research.
Now, let's see how WPS Office and AIPal can be great research partners:
Let's say that I have some PDFs which I have gathered from different sources. With WPS Office, these PDFs can be directly uploaded not just to extract key points but also to interact with the PDF with special help from WPS AI.
Step 1: Let's open the PDF article or research paper that we have downloaded on WPS Office.
Step 2: Now, click on the WPS AI widget at the top right corner of the screen.
Step 3: This will open the WPS PDF AI pane on the right side of the screen. Click on "Upload".
Step 4: Once the upload is complete, WPS PDF AI will return with the key points from the PDF article, which can then be copied to a fresh new document on WPS Writer.
Step 5: To interact further with the document, click on the "Inquiry" tab to talk with WPS AI and get more information on the contents of the PDF.
Research is incomplete without a Google search, but what exactly should you search for? AIPal can help you with these answers. AIPal is a Chrome extension that can help researchers make their Google searches and interactions with Chrome more effective and efficient. If you haven't installed AIPal on Chrome yet, go ahead and download the extension; it's completely free to use:
Step 1: Let's search for a term on Google related to our research.
Step 2: An AIPal widget will appear right next to the Google search bar, click on it.
Step 3: Upon clicking it, an AIPal window will pop up. In this window, you will find a more refined answer for your searched term, along with links most relevant to your search, providing a more refined search experience.
WPS AI can also be used to extract more information with the help of WPS Writer.
Step 1: We might have some information saved in a Word document, either from lectures or during preliminary research. We can use WPS AI within Writer to gain more insights.
Step 2: Select the entire text you want to summarize or understand better.
Step 3: Once the text is selected, a hover menu will appear. Click on the "WPS AI" icon in this menu.
Step 4: From the list of options, click on "Explain" to understand the content more deeply, or click on "Summarize" to shorten the paragraph.
Step 5: The results will be displayed in a small WPS AI window.
Develop the Thesis statement
To develop a strong thesis statement, start by formulating a central question your paper will address. For example, if your topic is about the impact of social media on mental health, your thesis statement might be:
"Social media use has a detrimental effect on mental health by increasing anxiety, depression, and loneliness among teenagers."
This statement is concise, contentious, and sets the stage for your research. With WPS AI, you can use the "Improve" feature to refine your thesis statement, ensuring it is clear, coherent, and impactful.
Write the First draft
Begin your first draft by focusing on maintaining forward momentum and clearly organizing your thoughts. Follow your outline as a guide, but be flexible if new ideas emerge. Here's a brief outline to get you started:
Using WPS AI’s "Make Longer" feature, you can quickly elaborate key ideas and points of your studies and articles into a descriptive format to include in your draft, saving time and ensuring clarity.
Compose Introduction, Body and Conclusion paragraphs
When writing a research paper, it’s essential to transform your key points into detailed, descriptive paragraphs. WPS AI can help you streamline this process by enhancing your key points, ensuring each section of your paper is well-developed and coherent. Here’s how you can use WPS AI to compose your introduction, body, and conclusion paragraphs:
Let's return to the draft and start composing our introduction. The introduction should provide the background of the research paper and introduce readers to what the research paper will explore.
If your introduction feels too brief or lacks depth, use WPS AI’s "Make Longer" feature to expand on key points, adding necessary details and enhancing the overall narrative.
Once the introduction is completed, the next step is to start writing the body paragraphs and the conclusion of our research paper. Remember, the body paragraphs will incorporate everything about your research: methodologies, challenges, results, and takeaways.
If this paragraph is too lengthy or repetitive, WPS AI’s "Make Shorter" feature can help you condense it without losing essential information.
Write the Second Draft
In the second draft, refine your arguments, ensure logical flow, and check for clarity. Focus on eliminating any unnecessary information, ensuring each paragraph supports your thesis statement, and improving transitions between ideas. Incorporate feedback from peers or advisors, and ensure all citations are accurate and properly formatted. The second draft should be more polished and coherent, presenting your research in a clear and compelling manner.
WPS AI’s "Improve Writing" feature can be particularly useful here to enhance the overall quality and readability of your paper.
WPS Spellcheck can assist you in correcting spelling and grammatical errors, ensuring your paper is polished and professional. This tool helps you avoid common mistakes and enhances the readability of your paper, making a significant difference in the overall quality.
Bonus Tips: How to Get Inspiration for your Research Paper- WPS AI
WPS Office is a phenomenal office suite that students find to be a major blessing. Not only is it a free office suite equipped with advanced features that make it competitive in the market, but it also includes a powerful AI that automates and enhances many tasks, including writing a research paper. In addition to improving readability with its AI Proofreader tool, WPS AI offers two features, "Insight" and "Inquiry", that can help you gather information and inspiration for your research paper:
Insight Feature:
The Insight feature provides deep insights and information on various topics and fields. It analyzes literature to extract key viewpoints, trends, and research directions. For instance, if you're writing a research paper on the impact of social media on mental health, you can use the Insight feature to gather a comprehensive overview of the latest studies, key arguments, and emerging trends in this field. This helps you build a solid foundation for your paper and ensure you are covering all relevant aspects.
Inquiry Feature:
The Inquiry feature allows you to ask specific questions related to your research topic. This helps you gather necessary background information and refine your research focus effectively. For example, if you need detailed information on how social media usage affects teenagers' self-esteem, you can use the Inquiry feature to ask targeted questions and receive relevant answers based on the latest research.
FAQs about writing a research paper
1. can any source be used for academic research.
No, it's essential to use credible and relevant sources. Here is why:
Developing a Strong Argument: Your research paper relies on evidence to substantiate its claims. Using unreliable sources can undermine your argument and harm the credibility of your paper.
Avoiding Inaccurate Information: The internet is abundant with data, but not all sources can be considered reliable. Credible sources guarantee accuracy.
2. How can I avoid plagiarism?
To avoid plagiarism, follow these steps:
Keep Records of Your Sources: Maintain a record of all the sources you use while researching. This helps you remember where you found specific ideas or phrases and ensures proper attribution.
Quote and Paraphrase Correctly: When writing a paper, use quotation marks for exact words from a source and cite them properly. When paraphrasing, restate the idea in your own words and include a citation to acknowledge the original source.
Utilize a Plagiarism Checker: Use a plagiarism detection tool before submitting your paper. This will help identify unintentional plagiarism, ensuring your paper is original and properly referenced.
3. How can I cite sources properly?
Adhere to the citation style guide (e.g., APA, MLA) specified by your instructor or journal. Properly citing all sources both within the text and in the bibliography or references section is essential for maintaining academic integrity and providing clear credit to the original authors. This practice also helps readers locate and verify the sources you've used in your research.
4. How long should a research paper be?
The length of a research paper depends on its topic and specific requirements. Generally, research papers vary between 4,000 to 6,000 words, with shorter papers around 2,000 words and longer ones exceeding 10,000 words. Adhering to the length requirements provided for academic assignments is essential. More intricate subjects or extensive research often require more thorough explanations, which can impact the overall length of the paper.
Write Your Research Paper with the Comfort of Using WPS Office
Writing a research paper involves managing numerous complicated tasks, such as ensuring the correct formatting, not missing any crucial information, and having all your data ready. The process of how to write a research paper is inherently challenging. However, if you are a student using WPS Office, the task becomes significantly simpler. WPS Office, especially with the introduction of WPS AI, provides all the resources you need to write the perfect research paper. Download WPS Office today and discover how it can transform your research paper writing experience for the better.
- 1. How to Write a Hook- Steps With Examples
- 2. How to Write a Conclusion - Steps with Examples
- 3. Free Graph Paper: Easy Steps to Make Printable Graph Paper PDF
- 4. How to Write a Proposal [ Steps & Examples]
- 5. How to Write an Abstract - Steps with Examples
- 6. How to Use WPS AI/Chatgpt to Write Research Papers: Guide for Beginners
15 years of office industry experience, tech lover and copywriter. Follow me for product reviews, comparisons, and recommendations for new apps and software.
all your locations, one content flow
content collaboration at scale
impress your clients and take on more
See customer stories
Create, plan, approve
Bring all your content together
Align your clients, team & content
Measure, report and strategize
- Pricing calculator for social media
Social media management
- Job title Quiz
Book a demo
Hello there
Noticed you’re on an iOS device. Get our mobile app for effortless planning on the go.
Noticed you’re on an Android device. Get our mobile app for effortless planning on the go.
Marketing calendar
Agency Workflows
Align your clients, team & content
Take a 1 minute tour to see how Planable works
For multi-location brands
For multi-brand companies
For agencies
“The team loved it from the start. Planable helps us overview the entire marketing efforts.“
Pricing Calculator for Social Media
Social Media Management Guide
Job Title Quiz
50+ Social Media Trends in 2022
Plan, review and schedule 6x faster
Planable for enterprises
Collaboration at scale
Planable for agencies
For you and your clients
Universal Content
For all marketing content
Table of contents
7 best google doc alternatives for content teams to improve collaboration.
Kseniia Volodina
Aug 14, 2024
No credit card required!
In the Microsoft Word era, Google Docs was a revolutionary content collaboration tool that rocked the content world. But content teams are now looking for Google Docs alternatives that further boost content workflow and improve their processes. After all, there’s a lot more to content marketing than creating and maintaining documents.
In this article, I’ve gathered 7 of the best Google Docs alternatives that help create and manage content more easily, faster, and better. Created with marketing teams in mind!
Why should you consider a Google Docs alternative?
Google Docs is a widely used cloud-based document editor known for its simplicity and collaboration features.
However, marketing teams in particular often seek alternatives to Google Docs, aiming for advanced functionality, more industry-specific capabilities, and better integration with other tools.
When evaluating any content management tool , I consider three core things: collaboration, approvals, and planning capabilities. Let’s see where Google Docs stands in these categories.
Collaboration
With features like chat, comment, and real-time editing, Google Docs allows multiple users to create and edit documents simultaneously. You can see what everyone else is typing, making it great for brainstorming or collective editing.
However, it lacks the advanced collaboration tools needed for larger teams or more complex projects, like internal notes. Not everything we discuss is meant for the client’s eyes, right?
Google Docs supports basic workflow features like commenting for approvals but lacks automated, multi-level approval processes.
Such automation plays a huge role in marketing teams, especially in agencies, speeding up the approval and ensuring nothing goes live without a green light.
Google Docs integrates with Google Calendar, allowing for basic planning and deadline tracking. However, it lacks direct embedding, content distribution capabilities, or more sophisticated project management features.
Pricing: Google Docs is free with a Google account, making it a great alternative to MS Word. Additional business features are available through Google Workspace subscriptions, starting at $6/month per user.
Here’s why your content team should consider a Google Docs alternative in 2024
- Enhanced functionality
Alternatives offer better customization, advanced editing tools, and superior document management capabilities.
- Comprehensive integration
Seamlessly integrate with social media platforms, CRM systems, project management tools, and other enterprise software.
- Scalability
Alternatives can offer better solutions for growing teams that need to manage larger datasets or more complex document structures.
- More industry-specific features
While Google Docs is a sturdy solution for text assets, some alternatives offer a more in-depth understanding of marketing processes, which is ideal if you want to enhance them.
1. Planable – best for content teams looking to improve collaboration and approvals
If you’re after collaboration capabilities beyond those in Google Docs, Planable is your ultimate social media collab tool . Out of all alternatives to Google Docs, Planable is the most collaboration-oriented, created specifically with marketing and content teams in mind.
Approvals, feedback in context, cross-company collaboration, publishing & media library features in Planable
Collaboration features like in-context feedback and custom approval workflows help marketing teams keep their processes smooth, ensuring the content moves down the pipeline as fast as possible. Bottlenecks? Haven’t heard that name in years.
Planable’s Universal Content feature allows you to create any text assets, from social media posts and blog articles to briefs, video scripts, and emails.
Real-time collaboration and feedback exchange are at the core of Planable.
Collaboration on a blog post in Planable
The platform is designed to streamline creative collaboration within the team and ease communication with external stakeholders or clients, making it a perfect Google Docs alternative for both internal marketing teams and agencies.
For example, you can leave internal feedback notes within your team as you work to perfect your next piece of content.
Internal notes in Planable
- In-context collaboration
Use real-time comments, suggestions, and annotations to pinpoint the pieces that need more work.
- Roles and permissions
Differentiate creators from approvers. Planable allows you to set up different roles to draw a line between your team members, external collaborators, and clients.
- Internal notes
Keep your comments, notes, and even whole posts hidden from your clients’ eyes. Some things are only meant for teammates.
- Notifications
Never miss a single beat with email and in-app notifications on mobile to jump into the discussion ASAP.
Planable takes collaboration up a notch with customizable approval workflows. Unlike the more generic Google Docs, Planable was created by marketers and for marketers.
The built-in content approval workflows allow you to get a green light in a few clicks rather than exchanging endless emails.
Set up specific permissions and roles and choose from four types of approval for each workspace you own: none , optional , mandatory , or multi – level .
Multi-level content approval flow in Planable
You can also set content to be locked on approval and for social media content to be posted automatically once approved, further cutting down the manual work.
Planable is also a content planning tool. In addition to document creation and collaboration, you can leverage Planable’s content calendar to plan and schedule your content.
I love how you can apply custom color-coded labels to differentiate between types of content or campaigns, then filter by those labels so you only see what’s relevant. You can use this calendar to map out entire content marketing strategies .
Content calendar view in Planable
- Multiple content views
Depending on your preferences and the platforms you’re working with, you can view your planned content as a calendar, list, feed, or grid.
- Social media scheduling
Create, preview, and schedule your social media posts in Planable’s content calendar. The posts will go live automatically at the specified time.
- Organize your content
Get a cohesive view of all your content efforts with custom colorful tags and filters to quickly find what you’re looking for and keep your content calendar neat.
Pricing: Planable offers 50 free posts with unlimited time to use them. After that, you can upgrade for $33/month and tailor the pricing according to the number of workspaces.
Drawbacks: Planable doesn’t directly integrate with CMS platforms for website publishing. However, you can collaborate on your blog publication or email and plan it in the content calendar.
Planable vs. Google Docs
- Planable seamlessly combines content creation and content management features. You can collaborate on content and schedule social media posts to go live across all your social media platforms.
- Unlike Google Docs, Planable offers built-in approval workflows , making streamlining your content processes within one platform easier.
- Planable provides a more tailored approach to organizing content, with tags, filters, and a built-in media library, making it easier to navigate your database.
Takeaway: Google Docs is a more generic solution, while Planable is more marketing and content-oriented.
Planable is more tailored to the specific needs of marketing teams, making it a Google Docs alternative that eases the writing process and comes with a handful of cool and industry-specific features.
2. ClickUp – best Google Docs alternative for teams looking to manage large projects
ClickUp is an extensive project management tool suitable for large marketing agencies or cross-functional teams. It allows users to create and assign tasks to manage any type of work.
You can also brainstorm ideas through whiteboards, create documents, and share them with multiple users to work on simultaneously.
A healthy combination of task tracking and document management makes ClickUp a good alternative to Google Docs if you’re looking for a single solution to manage big projects or tackle multiple clients.
Key features
- Task management features . Create, assign, and track tasks, report the timesheets, and set up automation to speed up your processes — long story short, leverage the project management side of ClickUp.
- Collaborative document editor. ClickUp Docs eases team collaboration by offering real-time editing, version history, and comment threading.
- Link documents to tasks. Create documents in ClickUp and connect them to specific tasks to keep track of all your assets. This is very useful if you need to find copy from several months ago, and all you remember is the campaign it was written for.
Pricing: ClickUp has a free plan and four paid options starting at $10/month.
Drawbacks: ClickUp is one of the better Google Doc alternatives for big projects that involve many teams. For small teams, it can be a bit of overkill. Users also mention that it’s kind of slow.
3. Zoho Writer – best lookalike Google Doc alternative
Zoho Writer is part of Zoho Workspace — like Google Workspace, but besides sheets, slides, and docs, it also integrates with Zoho’s CRM and other marketing solutions.
This tool feels like a simplified version of Google Docs. It’s a comfortable alternative to Google Docs for those who don’t actually want to switch but would like to get a couple of advanced features for content management.
For instance, Zoho Writer allows you to pause the document collaboration for a particular file, keeping it private until you’re ready to share it with your teammates. No Anonymous Tyrannosaurus peeking at your uncooked text.
- Review and approval tools . Leave comments and suggestions in the text. You can choose your own color to mark your changes and lock the document as final to prevent collaborators from changing it further.
- Third-party service integrations. Send your document directly to a signing tool like DocuSign or push it to WordPress. This is especially useful for teams working closely with blogs.
- Microsoft Word compatibility . If you have an MS Word document, you can easily upload it to Zoho’s word processor and seamlessly edit it there.
Pricing: You can use Zoho’s word processor for free. If you want to upgrade to Zoho’s Workspace, choose from three paid plans starting from €3/month/user.
Drawbacks: The spell checker and grammar suggestions from AI writing assistant Zia are somewhat mid.
4. Dropbox Paper – best alternative to Google Docs and Google Drive simultaneously
Dropbox Paper is an add-on to Dropbox’s file management system. You can create and manage documents with Paper without leaving the Dropbox interface. This enables team members to share documents and collaborate in real time on the same document.
All that unites in a neat storage ecosystem similar to Google Drive.
Paper’s integration with other Dropbox services and other third-party apps makes it a dynamic and versatile tool for marketing teams.
- Real-time collaboration . Work on the same document simultaneously with live content updates and comment streams to quickly exchange feedback and brainstorm ideas.
- Task management . You can assign tasks within documents, set due dates, and mention assignees without leaving the Paper environment.
- Third-party app integrations. You can integrate Dropbox Paper with other tools, like Canva or Slack. This is very useful if you want to take meeting notes and ensure everyone in the group chat gets them afterward.
Pricing: Dropbox Paper is available for all Dropbox accounts. Dropbox offers a free plan with up to 2GB of storage and four paid plans starting at $11.99/month.
Drawbacks: Dropbox Paper doesn’t offer desktop offline editing — you can only access the offline document editor through the mobile app, and the changes won’t sync across your devices.
5. Quip – best Google Docs alternative for sales teams
Quip is a team collaboration software tailored specifically for sales teams within the Salesforce ecosystem. Much like Google Docs and Google Workspace, Quip unites documents, spreadsheets, and chats into one bundle integrated with the core platform.
Its advanced features include integration with Salesforce CRM to streamline your sales processes with real-time data.
Quip is more specific than other Google Docs alternatives on this list due to its tight connection to Salesforce. However, if you heavily rely on Salesforce for CRM and sales management, Quit is your choice for document creation and collaboration.
- Version history . You can track all the changes within a document and return to previous versions if needed.
- Salesforce integration . Directly embed live data from Salesforce records into Quip documents, ensuring that the sales team always has the most current data at their fingertips.
- In-document chats . Discuss your data in context. Every document is equipped with a chat to enforce real-time collaboration between the team and individual team members.
Pricing: Quip has three paid plans starting at $10/month per user.
Drawbacks: Quip is one of the best Google Docs alternatives for Salesforce users. If you’re not an active Salesforce user, there are other alternatives to Google Docs you might find more useful.
6. Nuclino – best minimalistic Google Docs alternative
Nuclino is another real-time collaboration software for document creation, knowledge sharing, and simple project management.
In terms of the interface and general navigation, it’s a very lightweight Google Docs alternative. The interface is relatively clean and straightforward, with enough customization available.
I would say Nuclino is closer to Notion than Google Docs. It has many templates ready for use, from planning sprints and tasks to laying out buying personas, setting blog guidelines, and creating a company wiki.
- Real-time collaboration . Edit documents simultaneously with team members, seeing changes as they happen without any lag. Everyone can see your docs in the common workspace, making document sharing practically default.
- Wiki-style organization . Organizing documents in a visual graph helps you see connections between notes and swiftly navigate through large amounts of information.
- Lightweight interface . Nuclino’s clutter-free interface focuses purely on content, with no unnecessary features slowing you down.
Pricing: Nuclino has a free plan with up to 2 GB of space. You can upgrade for $6/month per user.
Drawbacks: Collaboration is limited to in-doc comments and mentions only, with no approval workflows, which can be big for creative teams.
7. Microsoft Office Online – best Google Docs alternative for Miscosoft adepts
Microsoft Office Online offers a cloud-based alternative to Google Docs that integrates seamlessly with the traditional Microsoft Office suite.
If your team is accustomed to Microsoft Word but wants to take the absolute same things online, this can be a solid option.
There’s not much to say about Microsoft Office Online — it’s the same old MS Word document functionality taken to cloud service.
- Familiar interface . For many big corporations, shifting to a new interface is challenging. Microsoft Word Online has the same interface we remember from high school, and it’s not bad.
- Collaboration . Work on your documents together with your colleagues through comments, annotations, and suggestions.
Pricing: Microsoft Word Online is available for free and as a part of the Microsoft 365 package, starting at $6/month per user.
Drawbacks: Microsoft Office Online and MS Word, though familiar, are quite clumsy, and nothing can change my mind.
Choose a Google Docs alternative that suits your process
Google Docs is a solid solution for written content. However, some alternatives can help you enhance your process rather than just get it done.
Planable has helped thousands of marketing teams create content faster and smoother. Try Planable with 50 free posts and see for yourself! There’s no pressure since those 50 posts are not time-limited — explore at your pace.
Content marketer with a background in journalism; digital nomad, and tech geek. In love with blogs, storytelling, strategies, and old-school Instagram. If it can be written, I probably wrote it.
Try Planable for free
I want to know more, Schedule a demo
THE EFFECTIVENESS OF E-LEARNING SYSTEM USING GOOGLE DOCS ON STUDENTS’ WRITING SKILLS IN NARRATIVE TEXT
- January 2023
- DIDAKTIKA Jurnal Kependidikan 16(2):173-183
- 16(2):173-183
- This person is not on ResearchGate, or hasn't claimed this research yet.
Discover the world's research
- 25+ million members
- 160+ million publication pages
- 2.3+ billion citations
No full-text available
To read the full-text of this research, you can request a copy directly from the authors.
- Muliyana Muliyana
- Muhammad Zuhri Dj
- Andi Muhammad Yauri
- Ela Nur Laili
- Tatik Muflihah
- Huda Suleiman Al Qunayeer
- Konstantinos BIKOS
- Manuela Pulimeno
- Zari Sadat SEYYEDREZAIE
- Hesamoddin Shahriari
- Ornprapat Suwantarathip
- Saovapa Wichadee
- Susan Dymock
- B A Dahnianti
- G Y Setyawan
- D Rochsantiningsih
- Recruit researchers
- Join for free
- Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up
Information
- Author Services
Initiatives
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
- Active Journals
- Find a Journal
- Proceedings Series
- For Authors
- For Reviewers
- For Editors
- For Librarians
- For Publishers
- For Societies
- For Conference Organizers
- Open Access Policy
- Institutional Open Access Program
- Special Issues Guidelines
- Editorial Process
- Research and Publication Ethics
- Article Processing Charges
- Testimonials
- Preprints.org
- SciProfiles
- Encyclopedia
Article Menu
- Subscribe SciFeed
- Recommended Articles
- Google Scholar
- on Google Scholar
- Table of Contents
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
JSmol Viewer
A review of computer vision-based crack detection methods in civil infrastructure: progress and challenges.
1. Introduction
2. crack detection combining traditional image processing methods and deep learning, 2.1. crack detection based on image edge detection and deep learning, 2.2. crack detection based on threshold segmentation and deep learning, 2.3. crack detection based on morphological operations and deep learning, 3. crack detection based on multimodal data fusion, 3.1. multi-sensor fusion, 3.2. multi-source data fusion, 4. crack detection based on image semantic understanding, 4.1. crack detection based on classification networks, 4.2. crack detection based on object detection networks, 4.3. crack detection based on segmentation networks.
Model | Improvement/Innovation | Backbone/Feature Extraction Architecture | Efficiency | Results |
---|---|---|---|---|
FCS-Net [ ] | Integrating ResNet-50, ASPP, and BN | ResNet-50 | - | MIoU = 74.08% |
FCN-SFW [ ] | Combining fully convolutional network (FCN) and structural forests with wavelet transform (SFW) for detecting tiny cracks | FCN | Computing time = 1.5826 s | Precision = 64.1% Recall = 87.22% F1 score = 68.28% |
AFFNet [ ] | Using ResNet101 as the backbone network, and incorporating two attention mechanism modules, namely VH-CAM and ECAUM | ResNet101 | Execution time = 52 ms | MIoU = 84.49% FWIoU = 97.07% PA = 98.36% MPA = 92.01% |
DeepLabv3+ [ ] | Replacing ordinary convolution with separable convolution; improved SE_ASSP module | Xception-65 | - | AP = 97.63% MAP = 95.58% MIoU = 81.87% |
U-Net [ ] | The parameters were optimized (the depths of the network, the choice of activation functions, the selection of loss functions, and the data augmentation) | Encoder and decoder | Analysis speed (1024 × 1024 pixels) = 0.022 s | Precision = 84.6% Recall = 72.5% F1 score = 78.1% IoU = 64% |
KTCAM-Net [ ] | Combined CAM and RCM; integrating classification network and segmentation network | DeepLabv3 | FPS = 28 | Accuracy = 97.26% Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
ADDU-Net [ ] | Featuring asymmetric dual decoders and dual attention mechanisms | Encoder and decoder | FPS = 35 | Precision = 68.9% Recall = 83.7% F1 score = 75.4% MIoU = 74.3% |
CGTr-Net [ ] | Optimized CG-Trans, TCFF, and hybrid loss functions | CG-Trans | - | Precision = 88.8% Recall = 88.3% F1 score = 88.6% MIoU = 89.4% |
PCSN [ ] | Using Adadelta as the optimizer and categorical cross-entropy as the loss function for the network | SegNet | Inference time = 0.12 s | mAP = 83% Accuracy = 90% Recall = 50% |
DEHF-Net [ ] | Introducing dual-branch encoder unit, feature fusion scheme, edge refinement module, and multi-scale feature fusion module | Dual-branch encoder unit | - | Precision = 86.3% Recall = 92.4% Dice score = 78.7% mIoU = 81.6% |
Student model + teacher model [ ] | Proposed a semi-supervised semantic segmentation network | EfficientUNet | - | Precision = 84.98% Recall = 84.38% F1 score = 83.15% |
5. Datasets
6. evaluation index, 7. discussion, 8. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.
Aspect | Combining Traditional Image Processing Methods and Deep Learning | Multimodal Data Fusion |
---|---|---|
Processing speed | Moderate—traditional methods are usually fast, but deep learning models may be slower, and the overall speed depends on the complexity of the deep learning model | Slower—data fusion and processing speed can be slow, especially with large-scale multimodal data, involving significant computational and data transfer overhead |
Accuracy | High—combines the interpretability of traditional methods with the complex pattern handling of deep learning, generally resulting in high detection accuracy | Typically higher—combining different data sources (e.g., images, text, audio) provides comprehensive information, improving overall detection accuracy |
Robustness | Strong—traditional methods provide background knowledge, enhancing robustness, but deep learning’s risk of overfitting may reduce robustness | Very strong—fusion of multiple data sources enhances the model’s adaptability to different environments and conditions, better handling noise and anomalies |
Complexity | High—integrating traditional methods and deep learning involves complex design and balancing, with challenges in tuning and interpreting deep learning models | High—involves complex data preprocessing, alignment, and fusion, handling inconsistencies and complexities from multiple data sources |
Adaptability | Strong—can adapt to different types of cracks and background variations, with deep learning models learning features from data, though it requires substantial labeled data | Very strong—combines diverse data sources, adapting well to various environments and conditions, and handling complex backgrounds and variations effectively |
Interpretability | Higher—traditional methods provide clear explanations, while deep learning models often lack interpretability; combining them can improve overall interpretability | Lower—fusion models generally have lower interpretability, making it difficult to intuitively explain how different data sources influence the final results |
Data requirements | High—deep learning models require a lot of labeled data, while traditional methods are more lenient, though deep learning still demands substantial data | Very high—requires large amounts of data from various modalities, and these data need to be processed and aligned effectively for successful fusion |
Flexibility | Moderate—combining traditional methods and deep learning handles various types of cracks, but may be limited in very complex scenarios | High—handles multiple data sources and different crack information, improving performance in diverse conditions through multimodal fusion |
Real-time capability | Poor—deep learning models are often slow to train and infer, making them less suitable for real-time detection, though combining with traditional methods can help | Poor—multimodal data fusion processing is generally slow, making it less suitable for real-time applications |
Maintenance cost | Moderate to high—deep learning models require regular updates and maintenance, while traditional methods have lower maintenance costs | High—involves ongoing maintenance and updates for multiple data sources, with complex data preprocessing and fusion processes |
Noise handling | Good—traditional methods effectively handle noise under certain conditions, and deep learning models can mitigate noise effects through training | Strong—multimodal fusion can complement information from different sources, improving robustness to noise and enhancing detection accuracy |
- Azimi, M.; Eslamlou, A.D.; Pekcan, G. Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors 2020 , 20 , 2778. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Han, X.; Zhao, Z. Structural surface crack detection method based on computer vision technology. J. Build. Struct. 2018 , 39 , 418–427. [ Google Scholar ]
- Kruachottikul, P.; Cooharojananone, N.; Phanomchoeng, G.; Chavarnakul, T.; Kovitanggoon, K.; Trakulwaranont, D. Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: A case of thailand’s department of highways. J. Civ. Struct. Health Monit. 2021 , 11 , 949–965. [ Google Scholar ] [ CrossRef ]
- Gehri, N.; Mata-Falcón, J.; Kaufmann, W. Automated crack detection and measurement based on digital image correlation. Constr. Build. Mater. 2020 , 256 , 119383. [ Google Scholar ] [ CrossRef ]
- Mohan, A.; Poobal, S. Crack detection using image processing: A critical review and analysis. Alex. Eng. J. 2018 , 57 , 787–798. [ Google Scholar ] [ CrossRef ]
- Liu, Y.; Fan, J.; Nie, J.; Kong, S.; Qi, Y. Review and prospect of digital-image-based crack detection of structure surface. China Civ. Eng. J. 2021 , 54 , 79–98. [ Google Scholar ]
- Hsieh, Y.-A.; Tsai, Y.J. Machine learning for crack detection: Review and model performance comparison. J. Comput. Civ. Eng. 2020 , 34 , 04020038. [ Google Scholar ] [ CrossRef ]
- Xu, Y.; Bao, Y.; Chen, J.; Zuo, W.; Li, H. Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Struct. Health Monit. 2019 , 18 , 653–674. [ Google Scholar ] [ CrossRef ]
- Wang, W.; Deng, L.; Shao, X. Fatigue design of steel bridges considering the effect of dynamic vehicle loading and overloaded trucks. J. Bridge Eng. 2016 , 21 , 04016048. [ Google Scholar ] [ CrossRef ]
- Zheng, K.; Zhou, S.; Zhang, Y.; Wei, Y.; Wang, J.; Wang, Y.; Qin, X. Simplified evaluation of shear stiffness degradation of diagonally cracked reinforced concrete beams. Materials 2023 , 16 , 4752. [ Google Scholar ] [ CrossRef ]
- Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986 , PAMI-8 , 679–698. [ Google Scholar ] [ CrossRef ]
- Otsu, N. A threshold selection method from gray-level histograms. Automatica 1975 , 11 , 23–27. [ Google Scholar ] [ CrossRef ]
- Sohn, H.G.; Lim, Y.M.; Yun, K.H.; Kim, G.H. Monitoring crack changes in concrete structures. Comput.-Aided Civ. Infrastruct. Eng. 2005 , 20 , 52–61. [ Google Scholar ] [ CrossRef ]
- Wang, P.; Qiao, H.; Feng, Q.; Xue, C. Internal corrosion cracks evolution in reinforced magnesium oxychloride cement concrete. Adv. Cem. Res. 2023 , 36 , 15–30. [ Google Scholar ] [ CrossRef ]
- Loutridis, S.; Douka, E.; Trochidis, A. Crack identification in double-cracked beams using wavelet analysis. J. Sound Vib. 2004 , 277 , 1025–1039. [ Google Scholar ] [ CrossRef ]
- Fan, C.L. Detection of multidamage to reinforced concrete using support vector machine-based clustering from digital images. Struct. Control Health Monit. 2021 , 28 , e2841. [ Google Scholar ] [ CrossRef ]
- Kyal, C.; Reza, M.; Varu, B.; Shreya, S. Image-based concrete crack detection using random forest and convolution neural network. In Computational Intelligence in Pattern Recognition: Proceedings of the International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), Held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24–25 April 2021 ; Springer: Singapore, 2022; pp. 471–481. [ Google Scholar ]
- Jia, H.; Lin, J.; Liu, J. Bridge seismic damage assessment model applying artificial neural networks and the random forest algorithm. Adv. Civ. Eng. 2020 , 2020 , 6548682. [ Google Scholar ] [ CrossRef ]
- Park, M.J.; Kim, J.; Jeong, S.; Jang, A.; Bae, J.; Ju, Y.K. Machine learning-based concrete crack depth prediction using thermal images taken under daylight conditions. Remote Sens. 2022 , 14 , 2151. [ Google Scholar ] [ CrossRef ]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015 , 521 , 436–444. [ Google Scholar ] [ CrossRef ]
- Liu, Z.; Cao, Y.; Wang, Y.; Wang, W. Computer vision-based concrete crack detection using u-net fully convolutional networks. Autom. Constr. 2019 , 104 , 129–139. [ Google Scholar ] [ CrossRef ]
- Li, G.; Ma, B.; He, S.; Ren, X.; Liu, Q. Automatic tunnel crack detection based on u-net and a convolutional neural network with alternately updated clique. Sensors 2020 , 20 , 717. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Chaiyasarn, K.; Buatik, A.; Mohamad, H.; Zhou, M.; Kongsilp, S.; Poovarodom, N. Integrated pixel-level cnn-fcn crack detection via photogrammetric 3d texture mapping of concrete structures. Autom. Constr. 2022 , 140 , 104388. [ Google Scholar ] [ CrossRef ]
- Li, S.; Zhao, X.; Zhou, G. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Comput.-Aided Civ. Infrastruct. Eng. 2019 , 34 , 616–634. [ Google Scholar ] [ CrossRef ]
- Zheng, X.; Zhang, S.; Li, X.; Li, G.; Li, X. Lightweight bridge crack detection method based on segnet and bottleneck depth-separable convolution with residuals. IEEE Access 2021 , 9 , 161649–161668. [ Google Scholar ] [ CrossRef ]
- Azouz, Z.; Honarvar Shakibaei Asli, B.; Khan, M. Evolution of crack analysis in structures using image processing technique: A review. Electronics 2023 , 12 , 3862. [ Google Scholar ] [ CrossRef ]
- Hamishebahar, Y.; Guan, H.; So, S.; Jo, J. A comprehensive review of deep learning-based crack detection approaches. Appl. Sci. 2022 , 12 , 1374. [ Google Scholar ] [ CrossRef ]
- Meng, S.; Gao, Z.; Zhou, Y.; He, B.; Djerrad, A. Real-time automatic crack detection method based on drone. Comput.-Aided Civ. Infrastruct. Eng. 2023 , 38 , 849–872. [ Google Scholar ] [ CrossRef ]
- Humpe, A. Bridge inspection with an off-the-shelf 360 camera drone. Drones 2020 , 4 , 67. [ Google Scholar ] [ CrossRef ]
- Truong-Hong, L.; Lindenbergh, R. Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds. Autom. Constr. 2022 , 135 , 104127. [ Google Scholar ] [ CrossRef ]
- Cusson, D.; Rossi, C.; Ozkan, I.F. Early warning system for the detection of unexpected bridge displacements from radar satellite data. J. Civ. Struct. Health Monit. 2021 , 11 , 189–204. [ Google Scholar ] [ CrossRef ]
- Bonaldo, G.; Caprino, A.; Lorenzoni, F.; da Porto, F. Monitoring displacements and damage detection through satellite MT-INSAR techniques: A new methodology and application to a case study in rome (Italy). Remote Sens. 2023 , 15 , 1177. [ Google Scholar ] [ CrossRef ]
- Zheng, Z.; Zhong, Y.; Wang, J.; Ma, A.; Zhang, L. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters. Remote Sens. Environ. 2021 , 265 , 112636. [ Google Scholar ] [ CrossRef ]
- Chen, X.; Zhang, X.; Ren, M.; Zhou, B.; Sun, M.; Feng, Z.; Chen, B.; Zhi, X. A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinf. 2024 , 128 , 103772. [ Google Scholar ] [ CrossRef ]
- Liu, F.; Liu, J.; Wang, L. Deep learning and infrared thermography for asphalt pavement crack severity classification. Autom. Constr. 2022 , 140 , 104383. [ Google Scholar ] [ CrossRef ]
- Liu, S.; Han, Y.; Xu, L. Recognition of road cracks based on multi-scale retinex fused with wavelet transform. Array 2022 , 15 , 100193. [ Google Scholar ] [ CrossRef ]
- Zhang, H.; Qian, Z.; Tan, Y.; Xie, Y.; Li, M. Investigation of pavement crack detection based on deep learning method using weakly supervised instance segmentation framework. Constr. Build. Mater. 2022 , 358 , 129117. [ Google Scholar ] [ CrossRef ]
- Dorafshan, S.; Thomas, R.J.; Maguire, M. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr. Build. Mater. 2018 , 186 , 1031–1045. [ Google Scholar ] [ CrossRef ]
- Munawar, H.S.; Hammad, A.W.; Haddad, A.; Soares, C.A.P.; Waller, S.T. Image-based crack detection methods: A review. Infrastructures 2021 , 6 , 115. [ Google Scholar ] [ CrossRef ]
- Chen, D.; Li, X.; Hu, F.; Mathiopoulos, P.T.; Di, S.; Sui, M.; Peethambaran, J. Edpnet: An encoding–decoding network with pyramidal representation for semantic image segmentation. Sensors 2023 , 23 , 3205. [ Google Scholar ] [ CrossRef ]
- Mo, S.; Shi, Y.; Yuan, Q.; Li, M. A survey of deep learning road extraction algorithms using high-resolution remote sensing images. Sensors 2024 , 24 , 1708. [ Google Scholar ] [ CrossRef ]
- Chen, D.; Li, J.; Di, S.; Peethambaran, J.; Xiang, G.; Wan, L.; Li, X. Critical points extraction from building façades by analyzing gradient structure tensor. Remote Sens. 2021 , 13 , 3146. [ Google Scholar ] [ CrossRef ]
- Liu, Y.; Yeoh, J.K.; Chua, D.K. Deep learning-based enhancement of motion blurred UAV concrete crack images. J. Comput. Civ. Eng. 2020 , 34 , 04020028. [ Google Scholar ] [ CrossRef ]
- Flah, M.; Nunez, I.; Ben Chaabene, W.; Nehdi, M.L. Machine learning algorithms in civil structural health monitoring: A systematic review. Arch. Comput. Methods Eng. 2021 , 28 , 2621–2643. [ Google Scholar ] [ CrossRef ]
- Li, G.; Li, X.; Zhou, J.; Liu, D.; Ren, W. Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network. Measurement 2021 , 176 , 109171. [ Google Scholar ] [ CrossRef ]
- Ali, R.; Chuah, J.H.; Talip, M.S.A.; Mokhtar, N.; Shoaib, M.A. Structural crack detection using deep convolutional neural networks. Autom. Constr. 2022 , 133 , 103989. [ Google Scholar ] [ CrossRef ]
- Wang, H.; Li, Y.; Dang, L.M.; Lee, S.; Moon, H. Pixel-level tunnel crack segmentation using a weakly supervised annotation approach. Comput. Ind. 2021 , 133 , 103545. [ Google Scholar ] [ CrossRef ]
- Zhu, J.; Song, J. Weakly supervised network based intelligent identification of cracks in asphalt concrete bridge deck. Alex. Eng. J. 2020 , 59 , 1307–1317. [ Google Scholar ] [ CrossRef ]
- Li, Y.; Bao, T.; Xu, B.; Shu, X.; Zhou, Y.; Du, Y.; Wang, R.; Zhang, K. A deep residual neural network framework with transfer learning for concrete dams patch-level crack classification and weakly-supervised localization. Measurement 2022 , 188 , 110641. [ Google Scholar ] [ CrossRef ]
- Yang, Q.; Shi, W.; Chen, J.; Lin, W. Deep convolution neural network-based transfer learning method for civil infrastructure crack detection. Autom. Constr. 2020 , 116 , 103199. [ Google Scholar ] [ CrossRef ]
- Dais, D.; Bal, I.E.; Smyrou, E.; Sarhosis, V. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Autom. Constr. 2021 , 125 , 103606. [ Google Scholar ] [ CrossRef ]
- Abdellatif, M.; Peel, H.; Cohn, A.G.; Fuentes, R. Combining block-based and pixel-based approaches to improve crack detection and localisation. Autom. Constr. 2021 , 122 , 103492. [ Google Scholar ] [ CrossRef ]
- Dan, D.; Dan, Q. Automatic recognition of surface cracks in bridges based on 2D-APES and mobile machine vision. Measurement 2021 , 168 , 108429. [ Google Scholar ] [ CrossRef ]
- Weng, X.; Huang, Y.; Wang, W. Segment-based pavement crack quantification. Autom. Constr. 2019 , 105 , 102819. [ Google Scholar ] [ CrossRef ]
- Kao, S.-P.; Chang, Y.-C.; Wang, F.-L. Combining the YOLOv4 deep learning model with UAV imagery processing technology in the extraction and quantization of cracks in bridges. Sensors 2023 , 23 , 2572. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Li, X.; Xu, X.; He, X.; Wei, X.; Yang, H. Intelligent crack detection method based on GM-ResNet. Sensors 2023 , 23 , 8369. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Choi, Y.; Park, H.W.; Mi, Y.; Song, S. Crack detection and analysis of concrete structures based on neural network and clustering. Sensors 2024 , 24 , 1725. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Guo, J.-M.; Markoni, H.; Lee, J.-D. BARNet: Boundary aware refinement network for crack detection. IEEE Trans. Intell. Transp. Syst. 2021 , 23 , 7343–7358. [ Google Scholar ] [ CrossRef ]
- Luo, J.; Lin, H.; Wei, X.; Wang, Y. Adaptive canny and semantic segmentation networks based on feature fusion for road crack detection. IEEE Access 2023 , 11 , 51740–51753. [ Google Scholar ] [ CrossRef ]
- Ranyal, E.; Sadhu, A.; Jain, K. Enhancing pavement health assessment: An attention-based approach for accurate crack detection, measurement, and mapping. Expert Syst. Appl. 2024 , 247 , 123314. [ Google Scholar ] [ CrossRef ]
- Liu, K.; Chen, B.M. Industrial UAV-based unsupervised domain adaptive crack recognitions: From database towards real-site infrastructural inspections. IEEE Trans. Ind. Electron. 2022 , 70 , 9410–9420. [ Google Scholar ] [ CrossRef ]
- Wang, W.; Hu, W.; Wang, W.; Xu, X.; Wang, M.; Shi, Y.; Qiu, S.; Tutumluer, E. Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network. Autom. Constr. 2021 , 124 , 103484. [ Google Scholar ] [ CrossRef ]
- Xu, Z.; Zhang, X.; Chen, W.; Liu, J.; Xu, T.; Wang, Z. Muraldiff: Diffusion for ancient murals restoration on large-scale pre-training. IEEE Trans. Emerg. Top. Comput. Intell. 2024 , 8 , 2169–2181. [ Google Scholar ] [ CrossRef ]
- Bradley, D.; Roth, G. Adaptive thresholding using the integral image. J. Graph. Tools 2007 , 12 , 13–21. [ Google Scholar ] [ CrossRef ]
- Sezgin, M.; Sankur, B.l. Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 2004 , 13 , 146–168. [ Google Scholar ]
- Kapur, J.N.; Sahoo, P.K.; Wong, A.K. A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 1985 , 29 , 273–285. [ Google Scholar ] [ CrossRef ]
- Pal, N.R.; Pal, S.K. A review on image segmentation techniques. Pattern Recognit. 1993 , 26 , 1277–1294. [ Google Scholar ] [ CrossRef ]
- Flah, M.; Suleiman, A.R.; Nehdi, M.L. Classification and quantification of cracks in concrete structures using deep learning image-based techniques. Cem. Concr. Compos. 2020 , 114 , 103781. [ Google Scholar ] [ CrossRef ]
- Mazni, M.; Husain, A.R.; Shapiai, M.I.; Ibrahim, I.S.; Anggara, D.W.; Zulkifli, R. An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and otsu method. Alex. Eng. J. 2024 , 92 , 310–320. [ Google Scholar ] [ CrossRef ]
- He, Z.; Xu, W. Deep learning and image preprocessing-based crack repair trace and secondary crack classification detection method for concrete bridges. Struct. Infrastruct. Eng. 2024 , 20 , 1–17. [ Google Scholar ] [ CrossRef ]
- He, T.; Li, H.; Qian, Z.; Niu, C.; Huang, R. Research on weakly supervised pavement crack segmentation based on defect location by generative adversarial network and target re-optimization. Constr. Build. Mater. 2024 , 411 , 134668. [ Google Scholar ] [ CrossRef ]
- Su, H.; Wang, X.; Han, T.; Wang, Z.; Zhao, Z.; Zhang, P. Research on a U-Net bridge crack identification and feature-calculation methods based on a CBAM attention mechanism. Buildings 2022 , 12 , 1561. [ Google Scholar ] [ CrossRef ]
- Kang, D.; Benipal, S.S.; Gopal, D.L.; Cha, Y.-J. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Autom. Constr. 2020 , 118 , 103291. [ Google Scholar ] [ CrossRef ]
- Lei, Q.; Zhong, J.; Wang, C. Joint optimization of crack segmentation with an adaptive dynamic threshold module. IEEE Trans. Intell. Transp. Syst. 2024 , 25 , 6902–6916. [ Google Scholar ] [ CrossRef ]
- Lei, Q.; Zhong, J.; Wang, C.; Xia, Y.; Zhou, Y. Dynamic thresholding for accurate crack segmentation using multi-objective optimization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, Italy, 18 September 2023 ; Springer: Cham, Switzerland, 2023; pp. 389–404. [ Google Scholar ]
- Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991 , 13 , 583–598. [ Google Scholar ] [ CrossRef ]
- Huang, H.; Zhao, S.; Zhang, D.; Chen, J. Deep learning-based instance segmentation of cracks from shield tunnel lining images. Struct. Infrastruct. Eng. 2022 , 18 , 183–196. [ Google Scholar ] [ CrossRef ]
- Fan, Z.; Lin, H.; Li, C.; Su, J.; Bruno, S.; Loprencipe, G. Use of parallel resnet for high-performance pavement crack detection and measurement. Sustainability 2022 , 14 , 1825. [ Google Scholar ] [ CrossRef ]
- Kong, S.Y.; Fan, J.S.; Liu, Y.F.; Wei, X.C.; Ma, X.W. Automated crack assessment and quantitative growth monitoring. Comput.-Aided Civ. Infrastruct. Eng. 2021 , 36 , 656–674. [ Google Scholar ] [ CrossRef ]
- Dang, L.M.; Wang, H.; Li, Y.; Park, Y.; Oh, C.; Nguyen, T.N.; Moon, H. Automatic tunnel lining crack evaluation and measurement using deep learning. Tunn. Undergr. Space Technol. 2022 , 124 , 104472. [ Google Scholar ] [ CrossRef ]
- Andrushia, A.D.; Anand, N.; Lubloy, E. Deep learning based thermal crack detection on structural concrete exposed to elevated temperature. Adv. Struct. Eng. 2021 , 24 , 1896–1909. [ Google Scholar ] [ CrossRef ]
- Dang, L.M.; Wang, H.; Li, Y.; Nguyen, L.Q.; Nguyen, T.N.; Song, H.-K.; Moon, H. Deep learning-based masonry crack segmentation and real-life crack length measurement. Constr. Build. Mater. 2022 , 359 , 129438. [ Google Scholar ] [ CrossRef ]
- Nguyen, A.; Gharehbaghi, V.; Le, N.T.; Sterling, L.; Chaudhry, U.I.; Crawford, S. ASR crack identification in bridges using deep learning and texture analysis. Structures 2023 , 50 , 494–507. [ Google Scholar ] [ CrossRef ]
- Dong, C.; Li, L.; Yan, J.; Zhang, Z.; Pan, H.; Catbas, F.N. Pixel-level fatigue crack segmentation in large-scale images of steel structures using an encoder–decoder network. Sensors 2021 , 21 , 4135. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Jian, L.; Chengshun, L.; Guanhong, L.; Zhiyuan, Z.; Bo, H.; Feng, G.; Quanyi, X. Lightweight defect detection equipment for road tunnels. IEEE Sens. J. 2023 , 24 , 5107–5121. [ Google Scholar ]
- Liang, H.; Qiu, D.; Ding, K.-L.; Zhang, Y.; Wang, Y.; Wang, X.; Liu, T.; Wan, S. Automatic pavement crack detection in multisource fusion images using similarity and difference features. IEEE Sens. J. 2023 , 24 , 5449–5465. [ Google Scholar ] [ CrossRef ]
- Alamdari, A.G.; Ebrahimkhanlou, A. A multi-scale robotic approach for precise crack measurement in concrete structures. Autom. Constr. 2024 , 158 , 105215. [ Google Scholar ] [ CrossRef ]
- Liu, H.; Kollosche, M.; Laflamme, S.; Clarke, D.R. Multifunctional soft stretchable strain sensor for complementary optical and electrical sensing of fatigue cracks. Smart Mater. Struct. 2023 , 32 , 045010. [ Google Scholar ] [ CrossRef ]
- Dang, D.-Z.; Wang, Y.-W.; Ni, Y.-Q. Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array. Constr. Build. Mater. 2024 , 411 , 134728. [ Google Scholar ] [ CrossRef ]
- Yan, M.; Tan, X.; Mahjoubi, S.; Bao, Y. Strain transfer effect on measurements with distributed fiber optic sensors. Autom. Constr. 2022 , 139 , 104262. [ Google Scholar ] [ CrossRef ]
- Shukla, H.; Piratla, K. Leakage detection in water pipelines using supervised classification of acceleration signals. Autom. Constr. 2020 , 117 , 103256. [ Google Scholar ] [ CrossRef ]
- Chen, X.; Zhang, X.; Li, J.; Ren, M.; Zhou, B. A new method for automated monitoring of road pavement aging conditions based on recurrent neural network. IEEE Trans. Intell. Transp. Syst. 2022 , 23 , 24510–24523. [ Google Scholar ] [ CrossRef ]
- Zhang, S.; He, X.; Xue, B.; Wu, T.; Ren, K.; Zhao, T. Segment-anything embedding for pixel-level road damage extraction using high-resolution satellite images. Int. J. Appl. Earth Obs. Geoinf. 2024 , 131 , 103985. [ Google Scholar ] [ CrossRef ]
- Park, S.E.; Eem, S.-H.; Jeon, H. Concrete crack detection and quantification using deep learning and structured light. Constr. Build. Mater. 2020 , 252 , 119096. [ Google Scholar ] [ CrossRef ]
- Yan, Y.; Mao, Z.; Wu, J.; Padir, T.; Hajjar, J.F. Towards automated detection and quantification of concrete cracks using integrated images and lidar data from unmanned aerial vehicles. Struct. Control Health Monit. 2021 , 28 , e2757. [ Google Scholar ] [ CrossRef ]
- Dong, Q.; Wang, S.; Chen, X.; Jiang, W.; Li, R.; Gu, X. Pavement crack detection based on point cloud data and data fusion. Philos. Trans. R. Soc. A 2023 , 381 , 20220165. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Kim, H.; Lee, S.; Ahn, E.; Shin, M.; Sim, S.-H. Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion. Struct. Health Monit. 2021 , 20 , 500–512. [ Google Scholar ] [ CrossRef ]
- Chen, J.; Lu, W.; Lou, J. Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic-rich building information model. Comput.-Aided Civ. Infrastruct. Eng. 2023 , 38 , 1079–1098. [ Google Scholar ] [ CrossRef ]
- Pozzer, S.; Rezazadeh Azar, E.; Dalla Rosa, F.; Chamberlain Pravia, Z.M. Semantic segmentation of defects in infrared thermographic images of highly damaged concrete structures. J. Perform. Constr. Facil. 2021 , 35 , 04020131. [ Google Scholar ] [ CrossRef ]
- Kaur, R.; Singh, S. A comprehensive review of object detection with deep learning. Digit. Signal Process. 2023 , 132 , 103812. [ Google Scholar ] [ CrossRef ]
- Sharma, V.K.; Mir, R.N. A comprehensive and systematic look up into deep learning based object detection techniques: A review. Comput. Sci. Rev. 2020 , 38 , 100301. [ Google Scholar ] [ CrossRef ]
- Zhang, L.; Yang, F.; Zhang, Y.D.; Zhu, Y.J. Road crack detection using deep convolutional neural network. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 3708–3712. [ Google Scholar ]
- Yang, C.; Chen, J.; Li, Z.; Huang, Y. Structural crack detection and recognition based on deep learning. Appl. Sci. 2021 , 11 , 2868. [ Google Scholar ] [ CrossRef ]
- Rajadurai, R.-S.; Kang, S.-T. Automated vision-based crack detection on concrete surfaces using deep learning. Appl. Sci. 2021 , 11 , 5229. [ Google Scholar ] [ CrossRef ]
- Kim, B.; Yuvaraj, N.; Sri Preethaa, K.; Arun Pandian, R. Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Neural Comput. Appl. 2021 , 33 , 9289–9305. [ Google Scholar ] [ CrossRef ]
- O’Brien, D.; Osborne, J.A.; Perez-Duenas, E.; Cunningham, R.; Li, Z. Automated crack classification for the CERN underground tunnel infrastructure using deep learning. Tunn. Undergr. Space Technol. 2023 , 131 , 104668. [ Google Scholar ]
- Chen, K.; Reichard, G.; Xu, X.; Akanmu, A. Automated crack segmentation in close-range building façade inspection images using deep learning techniques. J. Build. Eng. 2021 , 43 , 102913. [ Google Scholar ] [ CrossRef ]
- Dong, Z.; Wang, J.; Cui, B.; Wang, D.; Wang, X. Patch-based weakly supervised semantic segmentation network for crack detection. Constr. Build. Mater. 2020 , 258 , 120291. [ Google Scholar ] [ CrossRef ]
- Buatik, A.; Thansirichaisree, P.; Kalpiyapun, P.; Khademi, N.; Pasityothin, I.; Poovarodom, N. Mosaic crack mapping of footings by convolutional neural networks. Sci. Rep. 2024 , 14 , 7851. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Zhang, Y.; Zhang, L. Detection of pavement cracks by deep learning models of transformer and UNet. arXiv 2023 , arXiv:2304.12596. [ Google Scholar ] [ CrossRef ]
- Al-Huda, Z.; Peng, B.; Algburi, R.N.A.; Al-antari, M.A.; Rabea, A.-J.; Zhai, D. A hybrid deep learning pavement crack semantic segmentation. Eng. Appl. Artif. Intell. 2023 , 122 , 106142. [ Google Scholar ] [ CrossRef ]
- Shamsabadi, E.A.; Xu, C.; Rao, A.S.; Nguyen, T.; Ngo, T.; Dias-da-Costa, D. Vision transformer-based autonomous crack detection on asphalt and concrete surfaces. Autom. Constr. 2022 , 140 , 104316. [ Google Scholar ] [ CrossRef ]
- Huang, S.; Tang, W.; Huang, G.; Huangfu, L.; Yang, D. Weakly supervised patch label inference networks for efficient pavement distress detection and recognition in the wild. IEEE Trans. Intell. Transp. Syst. 2023 , 24 , 5216–5228. [ Google Scholar ] [ CrossRef ]
- Huang, G.; Huang, S.; Huangfu, L.; Yang, D. Weakly supervised patch label inference network with image pyramid for pavement diseases recognition in the wild. In Proceedings of the ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 6–11 June 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 7978–7982. [ Google Scholar ]
- Guo, J.-M.; Markoni, H. Efficient and adaptable patch-based crack detection. IEEE Trans. Intell. Transp. Syst. 2022 , 23 , 21885–21896. [ Google Scholar ] [ CrossRef ]
- König, J.; Jenkins, M.D.; Mannion, M.; Barrie, P.; Morison, G. Weakly-supervised surface crack segmentation by generating pseudo-labels using localization with a classifier and thresholding. IEEE Trans. Intell. Transp. Syst. 2022 , 23 , 24083–24094. [ Google Scholar ] [ CrossRef ]
- Al-Huda, Z.; Peng, B.; Algburi, R.N.A.; Al-antari, M.A.; Rabea, A.-J.; Al-maqtari, O.; Zhai, D. Asymmetric dual-decoder-U-Net for pavement crack semantic segmentation. Autom. Constr. 2023 , 156 , 105138. [ Google Scholar ] [ CrossRef ]
- Wen, T.; Lang, H.; Ding, S.; Lu, J.J.; Xing, Y. PCDNet: Seed operation-based deep learning model for pavement crack detection on 3d asphalt surface. J. Transp. Eng. Part B Pavements 2022 , 148 , 04022023. [ Google Scholar ] [ CrossRef ]
- Mishra, A.; Gangisetti, G.; Eftekhar Azam, Y.; Khazanchi, D. Weakly supervised crack segmentation using crack attention networks on concrete structures. Struct. Health Monit. 2024 , 23 , 14759217241228150. [ Google Scholar ] [ CrossRef ]
- Kompanets, A.; Pai, G.; Duits, R.; Leonetti, D.; Snijder, B. Deep learning for segmentation of cracks in high-resolution images of steel bridges. arXiv 2024 , arXiv:2403.17725. [ Google Scholar ]
- Liu, Y.; Yeoh, J.K. Robust pixel-wise concrete crack segmentation and properties retrieval using image patches. Autom. Constr. 2021 , 123 , 103535. [ Google Scholar ] [ CrossRef ]
- Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 580–587. [ Google Scholar ]
- Girshick, R. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 7–13 December 2015; pp. 1440–1448. [ Google Scholar ]
- Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 7–12 December 2015; Volume 28. [ Google Scholar ]
- He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 2961–2969. [ Google Scholar ]
- Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788. [ Google Scholar ]
- Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 7263–7271. [ Google Scholar ]
- Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018 , arXiv:1804.02767. [ Google Scholar ]
- Bochkovskiy, A.; Wang, C.-Y.; Liao, H.-Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv 2020 , arXiv:2004.10934. [ Google Scholar ]
- Wang, C.-Y.; Bochkovskiy, A.; Liao, H.-Y.M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 18–22 June 2023; pp. 7464–7475. [ Google Scholar ]
- Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; Berg, A.C. SSD: Single shot multibox detector. In Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016; Part I 14. Springer: Berlin/Heidelberg, Germany, 2016; pp. 21–37. [ Google Scholar ]
- Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 2980–2988. [ Google Scholar ]
- Xu, Y.; Li, D.; Xie, Q.; Wu, Q.; Wang, J. Automatic defect detection and segmentation of tunnel surface using modified mask R-CNN. Measurement 2021 , 178 , 109316. [ Google Scholar ] [ CrossRef ]
- Zhao, W.; Liu, Y.; Zhang, J.; Shao, Y.; Shu, J. Automatic pixel-level crack detection and evaluation of concrete structures using deep learning. Struct. Control Health Monit. 2022 , 29 , e2981. [ Google Scholar ] [ CrossRef ]
- Li, R.; Yu, J.; Li, F.; Yang, R.; Wang, Y.; Peng, Z. Automatic bridge crack detection using unmanned aerial vehicle and faster R-CNN. Constr. Build. Mater. 2023 , 362 , 129659. [ Google Scholar ] [ CrossRef ]
- Tran, T.S.; Nguyen, S.D.; Lee, H.J.; Tran, V.P. Advanced crack detection and segmentation on bridge decks using deep learning. Constr. Build. Mater. 2023 , 400 , 132839. [ Google Scholar ] [ CrossRef ]
- Zhang, J.; Qian, S.; Tan, C. Automated bridge crack detection method based on lightweight vision models. Complex Intell. Syst. 2023 , 9 , 1639–1652. [ Google Scholar ] [ CrossRef ]
- Ren, R.; Liu, F.; Shi, P.; Wang, H.; Huang, Y. Preprocessing of crack recognition: Automatic crack-location method based on deep learning. J. Mater. Civ. Eng. 2023 , 35 , 04022452. [ Google Scholar ] [ CrossRef ]
- Liu, Z.; Yeoh, J.K.; Gu, X.; Dong, Q.; Chen, Y.; Wu, W.; Wang, L.; Wang, D. Automatic pixel-level detection of vertical cracks in asphalt pavement based on gpr investigation and improved mask R-CNN. Autom. Constr. 2023 , 146 , 104689. [ Google Scholar ] [ CrossRef ]
- Li, Z.; Zhu, H.; Huang, M. A deep learning-based fine crack segmentation network on full-scale steel bridge images with complicated backgrounds. IEEE Access 2021 , 9 , 114989–114997. [ Google Scholar ] [ CrossRef ]
- Alipour, M.; Harris, D.K.; Miller, G.R. Robust pixel-level crack detection using deep fully convolutional neural networks. J. Comput. Civ. Eng. 2019 , 33 , 04019040. [ Google Scholar ] [ CrossRef ]
- Wang, S.; Pan, Y.; Chen, M.; Zhang, Y.; Wu, X. FCN-SFW: Steel structure crack segmentation using a fully convolutional network and structured forests. IEEE Access 2020 , 8 , 214358–214373. [ Google Scholar ] [ CrossRef ]
- Hang, J.; Wu, Y.; Li, Y.; Lai, T.; Zhang, J.; Li, Y. A deep learning semantic segmentation network with attention mechanism for concrete crack detection. Struct. Health Monit. 2023 , 22 , 3006–3026. [ Google Scholar ] [ CrossRef ]
- Sun, Y.; Yang, Y.; Yao, G.; Wei, F.; Wong, M. Autonomous crack and bughole detection for concrete surface image based on deep learning. IEEE Access 2021 , 9 , 85709–85720. [ Google Scholar ] [ CrossRef ]
- Wang, Z.; Leng, Z.; Zhang, Z. A weakly-supervised transformer-based hybrid network with multi-attention for pavement crack detection. Constr. Build. Mater. 2024 , 411 , 134134. [ Google Scholar ] [ CrossRef ]
- Chen, T.; Cai, Z.; Zhao, X.; Chen, C.; Liang, X.; Zou, T.; Wang, P. Pavement crack detection and recognition using the architecture of segNet. J. Ind. Inf. Integr. 2020 , 18 , 100144. [ Google Scholar ] [ CrossRef ]
- Bai, S.; Ma, M.; Yang, L.; Liu, Y. Pixel-wise crack defect segmentation with dual-encoder fusion network. Constr. Build. Mater. 2024 , 426 , 136179. [ Google Scholar ] [ CrossRef ]
- Wang, W.; Su, C. Semi-supervised semantic segmentation network for surface crack detection. Autom. Constr. 2021 , 128 , 103786. [ Google Scholar ] [ CrossRef ]
- Tabernik, D.; Šela, S.; Skvarč, J.; Skočaj, D. Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 2020 , 31 , 759–776. [ Google Scholar ] [ CrossRef ]
- König, J.; Jenkins, M.D.; Mannion, M.; Barrie, P.; Morison, G. Optimized deep encoder-decoder methods for crack segmentation. Digit. Signal Process. 2021 , 108 , 102907. [ Google Scholar ] [ CrossRef ]
- Wang, C.; Liu, H.; An, X.; Gong, Z.; Deng, F. Swincrack: Pavement crack detection using convolutional swin-transformer network. Digit. Signal Process. 2024 , 145 , 104297. [ Google Scholar ] [ CrossRef ]
- Lan, Z.-X.; Dong, X.-M. Minicrack: A simple but efficient convolutional neural network for pixel-level narrow crack detection. Comput. Ind. 2022 , 141 , 103698. [ Google Scholar ] [ CrossRef ]
- Salton, G. Introduction to Modern Information Retrieval ; McGraw-Hill: New York, NY, USA, 1983. [ Google Scholar ]
- Jenkins, M.D.; Carr, T.A.; Iglesias, M.I.; Buggy, T.; Morison, G. A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. In Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 3–7 September 2018; IEEE: Piscataway, NJ, USA; pp. 2120–2124. [ Google Scholar ]
- Tsai, Y.-C.; Chatterjee, A. Comprehensive, quantitative crack detection algorithm performance evaluation system. J. Comput. Civ. Eng. 2017 , 31 , 04017047. [ Google Scholar ] [ CrossRef ]
- Li, H.; Wang, J.; Zhang, Y.; Wang, Z.; Wang, T. A study on evaluation standard for automatic crack detection regard the random fractal. arXiv 2020 , arXiv:2007.12082. [ Google Scholar ]
Click here to enlarge figure
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Canny and YOLOv4 [ ] | Crack detection and measurement | Bridges | 1463 images 256 × 256 pixels | Smartphone and DJI UAV | Accuracy = 92% mAP = 92% | The Canny edge detector is affected by the threshold |
Canny and GM-ResNet [ ] | Crack detection, measurement, and classification | Road | 522 images 224 × 224 pixels | Concrete crack sub-dataset | Precision = 97.9% Recall = 98.9% F1 measure = 98.0% Accuracy in shadow conditions = 99.3% Accuracy in shadow-free conditions = 99.9% | Its detection performance for complex cracks is not yet perfect |
Sobel and ResNet50 [ ] | Crack detection | Concrete | 4500 images 100 × 100 pixels | FLIR E8 | Precision = 98.4% Recall = 88.7% F1 measure = 93.2% | - |
Sobel and BARNet [ ] | Crack detection and localization | Road | 206 images 800 × 600 pixels | CrackTree200 dataset | AIU = 19.85% ODS = 79.9% OIS = 81.4% | Hyperparameter tuning is needed to balance the penalty weights for different types of cracks |
Canny and DeepLabV3+ [ ] | Crack detection | Road | 2000 × 1500 pixels | Crack500 dataset | MIoU = 77.64% MAE = 1.55 PA = 97.38% F1 score = 63% | Detection performance deteriorating in dark environments or when interfering objects are present |
Canny and RetinaNet [ ] | Crack detection and measurement | Road | 850 images 256 × 256 pixels | SDNET 2018 dataset | Precision = 85.96% Recall = 84.48% F1 score = 85.21% | - |
Canny and Transformer [ ] | Crack detection and segmentation | Buildings | 11298 images 450 × 450 pixels | UAVs | GA = 83.5% MIoU = 76.2% Precision = 74.3% Recall = 75.2% F1 score = 74.7% | Resulting in a marginal increment in computational costs for various network backbones |
Canny and Inception-ResNet-v2 [ ] | Crack detection, measurement, and classification | High-speed railway | 4650 images 400 × 400 pixels | The track inspection vehicle | High severity level: Precision = 98.37% Recall = 93.82% F1 score = 95.99% Low severity level: Precision = 94.25% Recall = 98.39% F1 score = 96.23% | Only the average width was used to define the severity of the crack, and the influence of the length on the detection result was not considered |
Canny and Unet [ ] | Crack detection | Buildings | 165 images | - | SSIM = 14.5392 PSNR = 0.3206 RMSE = 0.0747 | Relies on a large amount of mural data for training and enhancement |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Otsu and Keras classifier [ ] | Crack detection, measurement, and classification | Concrete | 4000 images 227 × 227 pixels | Open dataset available | Classifiers accuracy = 98.25%, 97.18%, 96.17% Length error = 1.5% Width error = 5% Angle of orientation error = 2% | Only accurately quantify one single crack per image |
Otsu and TL MobileNetV2 [ ] | Crack detection, measurement, and classification | Concrete | 11435 images 224 × 224 pixels | Mendeley data—crack detection | Accuracy = 99.87% Recall = 99.74% Precision = 100% F1 score = 99.87% | Dependency on image quality |
Otsu, YOLOv7, Poisson noise, and bilateral filtering [ ] | Crack detection and classification | Bridges | 500 images 640 × 640 pixels | Dataset | Training time = 35 min Inference time = 8.9 s Target correct rate = 85.97% Negative sample misclassification rate = 42.86% | It does not provide quantified information such as length and area |
Adaptive threshold and WSIS [ ] | Crack detection | Road | 320 images 3024 × 4032 pixels | Photos of cracks | Recall = 90% Precision = 52% IoU = 50% F1 score = 66% Accuracy = 98% | For some small cracks (with a width of less than 3 pixels), model can only identify the existence of small cracks, but it is difficult to depict the cracks in detail |
Adaptive threshold and U-GAT-IT [ ] | Crack detection | Road | 300 training images and237 test images | DeepCrack dataset | Recall = 79.3% Precision = 82.2% F1 score = 80.7% | Further research is needed to address the interference caused by factors such as small cracks, road shadows, and water stains |
Local thresholding and DCNN [ ] | Crack detection | Concrete | 125 images 227 × 227 pixels | Cameras | Accuracy = 93% Recall = 91% Precision = 92% F1 score = 91% | - |
Otsu and Faster R-CNN [ ] | Crack detection, localization, and quantification | Concrete | 100 images 1920 × 1080 pixels | Nikon d7200 camera and Galaxy s9 camera | AP = 95% mIoU = 83% RMSE = 2.6 pixels Length accuracy = 93% | The proposed method is useful for concrete cracks only; its applicability for the detection of other crack materials might be limited |
Adaptive Dynamic Thresholding Module (ADTM) and Mask DINO [ ] | Crack detection and segmentation | Road | 395 images 2000 × 1500 pixels | Crack500 | mIoU = 81.3% mAcc = 96.4% gAcc = 85.0% | ADTM module can only handle binary classification problems |
Dynamic Thresholding Branch and DeepCrack [ ] | Crack detection and classification | Bridges | 3648 × 5472 pixels | Crack500 | mIoU = 79.3% mAcc = 98.5% gAcc = 86.6% | Image-level thresholds lead to misclassification of the background |
Method | Features | Domain | Dataset | Image Device/Source | Results | Limitations |
---|---|---|---|---|---|---|
Morphological closing operations and Mask R-CNN [ ] | Crack detection | Tunnel | 761 images 227 × 227 pixels | MTI-200a | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% | Relatively small compared to the needs of the required sample size for universal conditions |
Morphological operations and Parallel ResNet [ ] | Crack detection and measurement | Road | 206 images (CrackTree200) 800 × 600 pixels and 118 images (CFD) 320 × 480 pixels | CrackTree200 dataset and CFD dataset | CrackTree200: Precision = 94.27% Recall = 92.52% F1 = 93.08% CFD: Precision = 96.21% Recall = 95.12% F1 = 95.63% | The method was only performed on accurate static images |
Closing and CNN [ ] | Crack detection, measurement, and classification | Concrete | 3208 images 256 × 256 pixels or 128 × 128 pixels | Hand-held DSLR cameras | Relative error = 5% Accuracy > 95% Loss < 0.1 | The extraction of the cracks’ edge will have a larger influence on the results |
Dilation and TunnelURes [ ] | Crack detection, measurement, and classification | Tunnel | 6810 images image sizes vary 10441 × 2910 to 50739 × 3140 | Night 4K line-scan cameras | AUC = 0.97 PA = 0.928 IoU = 0.847 | The medial-axis skeletonization algorithm created many errors because it was susceptible to the crack intersection and the image edges where the crack’s representation changed |
Opening, closing, and U-Net [ ] | Crack detection, measurement, and classification | Concrete | 200 images 512 × 512 pixels | Canon SX510 HS camera | Precision = 96.52% Recall = 93.73% F measure = 96.12% Accuracy = 99.74% IoU = 78.12% | It can only detect the other type of cracks which have the same crack geometry as that of thermal cracks |
Morphological operations and DeepLabV3+ [ ] | Crack detection and measurement | Masonry structure | 200 images 780 × 355 pixels and 2880 × 1920 pixels | Internet, drones, and smartphones | IoU = 0.97 F1 score = 98% Accuracy = 98% | The model will not detect crack features that do not appear in the dataset (complicated cracks, tiny cracks, etc.) |
Erosion, texture analysis techniques, and InceptionV3 [ ] | Crack detection and classification | Bridges | 1706 images 256 × 256 pixels | Cameras | F1 score = 93.7% Accuracy = 94.07% | - |
U-Net, opening, and closing operations [ ] | Crack detection and segmentation | Bridges | 244 images 512 × 512 pixels | Cameras | mP = 44.57% mR = 53.13% Mf1 = 42.79% mIoU = 64.79% | The model lacks generality, and there are cases of false detection |
Sensor Type | Fusion Method | Advantages | Disadvantages | Application Scenarios |
---|---|---|---|---|
Optical sensor [ ] | Data-level fusion | High resolution, rich in details | Susceptible to light and occlusion | Surface crack detection, general environments |
Thermal sensor [ ] | Feature level fusion | Suitable for nighttime or low-light environments, detects temperature changes | Low resolution, lack of detail | Nighttime detection, heat-sensitive areas, large-area surface crack detection |
Laser sensor [ ] | Data-level fusion and feature level fusion | High-precision 3D point cloud data, accurately measures crack morphology | High equipment cost, complex data processing | Complex structures, precise measurements |
Strain sensor [ ] | Feature level fusion and decision-level fusion | High sensitivity to structural changes; durable | Requires contact with the material; installation complexity | Monitoring structural health in bridges and buildings; detecting early-stage crack development |
Ultrasonic sensor [ ] | Data-level fusion and feature level fusion | Detects internal cracks in materials, strong penetration | Affected by material and geometric shape, limited resolution | Internal cracks, metal material detection |
Optical fiber sensor [ ] | Feature level fusion | High sensitivity to changes in material properties, non-contact measurement | Affected by environmental conditions, requires calibration | Surface crack detection, structural health monitoring |
Vibration sensor [ ] | Data-level fusion | Detects structural vibration characteristics, strong adaptability | Affected by environmental vibrations, requires complex signal processing | Dynamic crack monitoring, bridges and other structures |
Multispectral satellite sensor [ ] | Data-level fusion | Rich spectral information | Limited spectral resolution, weather- and lighting-dependent, high cost | Pavement crack detection, bridge and infrastructure monitoring, building facade inspection |
High-resolution satellite sensors [ ] | Data-level fusion and feature level fusion | High spatial resolution, wide coverage, frequent revisit times, rich information content | Weather dependency, high cost, data processing complexity, limited temporal resolution | Road and pavement crack detection, bridge and infrastructure monitoring, urban building facade inspection, railway and highway crack monitoring |
Scale | Dataset/(Pixels × Pixels) | References |
---|---|---|
Image-based | 227 × 227 | [ , , , ] |
224 × 224 | [ ] | |
256 × 256 | [ ] | |
416 × 416 | [ ] | |
512 × 512 | [ ] | |
Patch-based | 128 × 128 | [ , ] |
200 × 200 | [ ] | |
224 × 224 | [ , , , , ] | |
227 × 227 | [ ] | |
256 × 256 | [ , ] | |
300 × 300 | [ , ] | |
320 × 480 | [ , ] | |
544 × 384 | [ ] | |
512 × 512 | [ , , , ] | |
584 × 384 | [ ] |
Model | Improvement/Innovation | Dataset | Backbone | Results |
---|---|---|---|---|
Faster R-CNN [ ] | Combined with drones for crack detection | 2000 images 5280 × 2970 pixels | VGG-16 | Precision = 92.03% Recall = 96.26% F1 score = 94.10% |
Faster R-CNN [ ] | Double-head structure is introduced, including an independent fully connected head and a convolution head | 1622 images 1612 × 1947 pixels | ResNet50 | AP = 47.2% |
Mask R-CNN [ ] | The morphological closing operation was incorporated into the M-R-101-FPN model to form an integrated model | 761 images 227 × 227 pixels | ResNets and VGG | Balanced accuracy = 81.94% F1 score = 68.68% IoU = 52.72% |
Mask R-CNN [ ] | PAFPN module and edge detection branch was introduced | 9680 images 1500 × 1500 pixels | ResNet-FPN | Precision = 92.03% Recall = 96.26% AP = 94.10% mAP = 90.57% Error rate = 0.57% |
Mask R-CNN [ ] | FPN structure introduces side join method and combines FPN with ResNet-101 to change RoI-Pooling layer to RoI-Align layer | 3430 images 1024 × 1024 pixels | ResNet101 | AP = 83.3% F1 score = 82.4% Average error = 2.33% mIoU = 70.1% |
YOLOv3-tiny [ ] | A structural crack detection and quantification method combined with structured light is proposed | 500 images 640 × 640 pixels | Darknet-53 | Accuracy = 94% Precision = 98% |
YOLOv4 [ ] | Some lightweight networks were used instead of the original backbone feature extraction network, and DenseNet, MobileNet, and GhostNet were selected for the lightweight networks | 800 images 416 × 416 pixels | DenseNet, MobileNet v1, MobileNet v2, MobileNet v3, and GhostNet | Precision = 93.96% Recall = 90.12% F1 score = 92% |
YOLOv4 [ ] | - | 1463 images 256 × 256 pixels | Darknet-53 | Accuracy = 92% mAP = 92% |
Datasets Name | Number of Images | Image Resolution | Manual Annotation | Scope of Applicability | Limitations |
---|---|---|---|---|---|
CrackTree200 [ ] | 206 images | 800 × 600 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | With only 200 images, the dataset’s relatively small size can hinder the model’s ability to generalize across diverse conditions, potentially leading to overfitting on the specific examples provided |
Crack500 [ ] | 500 images | 2000 × 1500 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | Limited number of images compared to larger datasets, which might affect the generalization of models trained on this dataset |
SDNET 2018 [ ] | 56000 images | 256 × 256 pixels | Pixel-level annotations for cracks | Crack classification and segmentation | The dataset’s focus on concrete surfaces may limit the model’s performance when applied to different types of surfaces or structures |
Mendeley data—crack detection [ ] | 40000 images | 227 × 227 pixels | Pixel-level annotations for cracks | Crack classification | The dataset might not cover all types of cracks or surface conditions, which can limit its applicability to a wide range of real-world scenarios |
DeepCrack [ ] | 2500 images | 512 × 512 pixels | Annotations for cracks | Crack segmentation | The resolution might limit the ability of models to capture very small or subtle crack features |
CFD [ ] | 118 images | 320 × 480 pixels | Pixel-level annotations for cracks | Crack segmentation | The dataset contains a limited number of data samples, which may limit the generalization ability of the model |
CrackTree260 [ ] | 260 images | 800 × 600 pixels and 960 × 720 pixels | Pixel-level labeling, bounding boxes, or other crack markers | Object detection and segmentation | Because the dataset is small, it can be easy for the model to overfit the training data, especially if you’re using a complex model |
CrackLS315 [ ] | 315 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The small size of the dataset may make the model perform poorly in complex scenarios, especially when encountering different types of cracks or uncommon crack features |
Stone331 [ ] | 331 images | 512 × 512 pixels | Pixel-level segmentation mask or bounding box | Object detection and segmentation | The relatively small number of images limits the generalization ability of the model, especially in deep learning tasks where smaller datasets tend to lead to overfitting |
Index | Index Value and Calculation Formula | Curve |
---|---|---|
True positive | - | |
False positive | - | |
True negative | - | |
False negative | - | |
Precision | PRC | |
Recall | PRC, ROC curve | |
F1 score | F1 score curve | |
Accuracy | Accuracy vs. threshold curve | |
Average precision | PRC | |
Mean average precision | - | |
IoU | IoU distribution curve, precision-recall curve with IoU thresholds |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Share and Cite
Yuan, Q.; Shi, Y.; Li, M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sens. 2024 , 16 , 2910. https://doi.org/10.3390/rs16162910
Yuan Q, Shi Y, Li M. A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges. Remote Sensing . 2024; 16(16):2910. https://doi.org/10.3390/rs16162910
Yuan, Qi, Yufeng Shi, and Mingyue Li. 2024. "A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges" Remote Sensing 16, no. 16: 2910. https://doi.org/10.3390/rs16162910
Article Metrics
Article access statistics, further information, mdpi initiatives, follow mdpi.
Subscribe to receive issue release notifications and newsletters from MDPI journals
IMAGES
COMMENTS
First, you can click the Explore icon at the bottom-right corner of the Google Docs screen. Second, you can click Tools > Explore from the menu. (These first two options to open Explore are convenient if you plan to research a variety of topics and simply want to open the tool.) Finally, you can open Explore and go directly to your topic.
Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.
Click the "Center" button, and then the "Bold" button. Next, type the paper's title (see Figure 1). Figure 1. Press the enter key, and click the "left align" button. 3. Before setting the first-line indent for the rest of the paper, click the "View" drop-down menu, and make sure "Show ruler" is checked (see Figure 2).
Head to the Google Docs homepage and click Template gallery in the top-right. Head to your account's template gallery. Google; William Antonelli/Insider. 2. Scroll down the templates page until ...
How to Set Up APA Format in Google Docs. Step 1: Configure Margin Settings. Step 2: Add Page Headers. Step 3: Set up the APA Format for Title Page in Google Docs. Step 4: Insert an Abstract Page. Step 5: Type the Full Paper Title & Start Writing. How to Format References for APA Style.
**Updated version for MLA 9: https://youtu.be/YiW0iEBGFB8**Goes through heading, header, spacing and Works Cited page.
Here's the Google way to do research reports and papers in 10 steps. And please feel free to add your own nuances, changes or additional steps in the comments below! 1. Get started quickly. ... Google Docs has an image search built in that pulls Creative Commons and public domain images from databases on the web. These images are licensed for ...
Check out the Basic Formatting page for quick, easy instructions on how to format your paper using Google Docs. If you don't already have a Google account set up, click this link to create a free account. With a Google account you can access the professional suite for Google Docs, Sheets, Presentations, and more.
From your Google Doc, 1) click on the Line Spacing icon (looks like lines with an arrow up and down) and 2) select Double from the drop down menu. OR From your Google Doc, 1) click on Format from the menu, 2) put your cursor on Line Spacing and 3) select Double from the pop out menu.
With Google Docs, you can easily find and then add citations to all of your research papers. Fire up your browser, head over to Google Docs, and open up a document. At the bottom of the right side, click the "Explore" icon to open up a panel on the right. Alternatively, press Ctrl+Alt+Shift+I on Windows/Chrome OS or Cmd+Option+Shift+I on macOS ...
In the text of your document, place your cursor where you want the citation to appear. In the Citations sidebar, hover over the source you want to cite. A Cite button appears on the side of the citation source. Click Cite . The source appears in your selected style within the text of your document.
To begin organizing your document in Google Docs, first set up the page size to letter-sized 8 ½" by 11″ or A4 sized paper. Here are some tips for setting up the structure: Go to File > Page Setup in the main menu bar; Choose from one of four options-U.S., Canadian Legal, U.K., & Australian;
Paperpile also offers a free citation generator for Google Docs that makes it a breeze to find, cite, and style academic sources within your document. 6. Leave Time to Make it Pretty. One thing Google Docs cannot do is make a truly visually striking document. It suffers from the same limitations that all WYSIWYG editors do - it can't be both ...
Everything you need to know about formatting your research paper on Google Docs.
The Google Docs Research Tool. While editing your paper in Google Docs, click the Tools menu, then Research: This will bring up a Research panel along the right-hand edge of your browser window: The Research Tool lets you conduct internet research and incorporate the results into your document, without having to leave Google Docs.
Add our citation app in one click from the Google Docs add-on store! Writing a paper in Google Docs the Paperpile way works like this: Install the Google Docs add-on. Invite your colleagues to your documents and ask them to install the add-on. Add citations, here's our cheat sheet. Organize your papers in one place. Try Paperpile.
Step 1: Open the 'Tools' Menu. Click on the 'Tools' menu at the top of your Google Doc. In the 'Tools' menu, you'll find various options to enhance your document. For citations, you'll be using the 'Citations' feature.
Publications. Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. Google publishes hundreds of research papers each year. Publishing our work enables us to collaborate and share ideas with, as well as learn from, the broader scientific community.
Then, put the blinking cursor at the point in the document where you would like to add a citation. Hover over the result you'd like to cite here. Click the quotation button will appear to the top right of the result. Once clicked, two things will happen. First, Google Docs will add a superscript number where your cursor is to identify it.
Create and edit web-based documents, spreadsheets, and presentations. Store documents online and access them from any computer.
Paperpile is a full-featured reference manager right in your Google Doc: - Create a perfectly formatted bibliography ready for submission of your paper. - Insert citations with one click. - Supports in-text citations and footnote citations. - APA, MLA, Chicago and more than 7000 journal specific citation styles.
Research Paper Template. The fastest (and smartest) way to craft a research paper that showcases your project and earns you marks. Available in Google Doc, Word & PDF format. 4.9 star rating, 5000+ downloads. Download Now (Instant access)
Go to File > Make a Copy and navigate to the file where you would like to save it.] [This template is designed to help you quickly format research papers according to Modern Language Association (MLA) style conventions; it is based on the MLA Handbook for Writers of Research Papers (7th edition). To use this template, highlight and replace all ...
A research paper explores and evaluates previously and newly gathered information on a topic, then offers evidence for an argument. It follows academic writing standards, and virtually every college student will write at least one. Research papers are also integral to scientific fields, among others, as the most reliable way to share knowledge.
Step 1: Let's search for a term on Google related to our research. Step 2: An AIPal widget will appear right next to the Google search bar, click on it. ... Writing a research paper involves managing numerous complicated tasks, such as ensuring the correct formatting, not missing any crucial information, and having all your data ready. ...
Google Docs integrates with Google Calendar, allowing for basic planning and deadline tracking. However, it lacks direct embedding, content distribution capabilities, or more sophisticated project management features. Pricing: Google Docs is free with a Google account, making it a great alternative to MS Word. Additional business features are ...
Download Citation | THE EFFECTIVENESS OF E-LEARNING SYSTEM USING GOOGLE DOCS ON STUDENTS' WRITING SKILLS IN NARRATIVE TEXT | This paper investigates the effectiveness of e-learning system by ...
Based on the main research methods of the 120 documents, we classify them into three crack detection methods: fusion of traditional methods and deep learning, multimodal data fusion, and semantic image understanding. ... which is crucial for safe operation. In this paper, Web of Science (WOS) and Google Scholar were used as literature search ...