A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision

A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision Zhanfeng Qi, Guoliang Li, Zhiyi Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3771187/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Automated identification of concrete cracks is essential for ensuring infrastructure integrity and sustained safety. However, the irregularities and non-uniformity of concrete cracks present challenges in achieving a thorough, automated crack assessment meeting current specifications while reducing human involvement. Although relying on crack width to evaluate the degree of cracking has been widely accepted, the measurement of crack width is still limited by the measurement position, and the detection results often require additional processing, lacking intuitive representation. This paper proposes an innovative approach for a complete end-to-end detection system for assessing concrete crack grades, which includes a detection methodology and an evaluation framework. The detection method employs 227×227×3 detection cells trained using convolutional neural networks, each assigned distinct labels to slide and identify cracks. Simultaneously, the evaluation framework translates the concrete current specifications into computer-executable procedures and generates the quantitative evaluation of single and multiple cracks in concrete. The experimental findings indicate the models using fine-tuned AlexNet and VGG16 trained on the self-generated Crack-1000 dataset, generate a low error recognition rate of 5.2% and 2.4% respectively. The applications demonstrate the trained model and evaluation framework can accurately identify crack paths and provide a crack label that matches the actual situation. Compared with traditional methods, the method proposed in this article has superior recognition accuracy and end-to-end detection ability, eliminating the necessity of manual parameter adjustment. This innovative visual solution provides an effective method for detecting and evaluating cracks in concrete structures. concrete crack end-to-end detection convolutional neural networks detection cell evaluation framework practical application. Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3771187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264198313,"identity":"882fa9aa-9a7d-49a3-8bb1-5bfa2f0eb67b","order_by":0,"name":"Zhanfeng Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3RMWsCMRTA8RcC6aLnmnD0/ArvuEEEP0zEoWunrgqFTHXX7+CQ0fHkwFtOb82oFNwODm4qtNDYgu0UbxTMHxJeQn5TAHy+W4wCs3t0nlLg55u0HUnsYrIlgQvp4O/5Ggnyh9PhY43RoLdq6qGCKDCSNM8OIjJ4iucFJsNFpVEoSISRNFw4CGaw5V31OdZmp6UldpCMdpyEKPGlcKpNcUgtmbYglIVdhRLLNzKzROI1IjLGwkeFsTYsAb7n8bI4voYuEpT5SVQK+1hm7w1/GfWDfLJpXOTvF7gEyn8+k8xc4B/ppUBq91ufz+e7074BgatMAKyOSOIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2796-0277","institution":"China Railway First Survey and Design Institute Group Co Ltd","correspondingAuthor":true,"prefix":"","firstName":"Zhanfeng","middleName":"","lastName":"Qi","suffix":""},{"id":264198314,"identity":"bb52f815-6ecb-4504-b78e-cf8041843312","order_by":1,"name":"Guoliang Li","email":"","orcid":"","institution":"China Railway First Survey and Design Institute Group Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Li","suffix":""},{"id":264198315,"identity":"f6d3266b-9d49-44b3-99bb-8952d75dbb66","order_by":2,"name":"Zhiyi Huang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2023-12-18 09:51:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3771187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3771187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58567129,"identity":"6cf0067e-16dc-4de5-b175-2d0efa54cf39","added_by":"auto","created_at":"2024-06-18 10:13:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsrevisedv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3771187/v1_covered_00f29335-1a10-4b37-99ef-7828878dc862.pdf"}],"financialInterests":"","formattedTitle":"A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"concrete crack, end-to-end detection, convolutional neural networks, detection cell, evaluation framework, practical application.","lastPublishedDoi":"10.21203/rs.3.rs-3771187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3771187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutomated identification of concrete cracks is essential for ensuring infrastructure integrity and sustained safety. However, the irregularities and non-uniformity of concrete cracks present challenges in achieving a thorough, automated crack assessment meeting current specifications while reducing human involvement. Although relying on crack width to evaluate the degree of cracking has been widely accepted, the measurement of crack width is still limited by the measurement position, and the detection results often require additional processing, lacking intuitive representation. This paper proposes an innovative approach for a complete end-to-end detection system for assessing concrete crack grades, which includes a detection methodology and an evaluation framework. The detection method employs 227\u0026times;227\u0026times;3 detection cells trained using convolutional neural networks, each assigned distinct labels to slide and identify cracks. 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