Research on Concrete Crack Detection based on Fourier Image Enhancement and Convolutional Neural Network

Research on Concrete Crack Detection based on Fourier Image Enhancement and Convolutional Neural Network | 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 Case Report Research on Concrete Crack Detection based on Fourier Image Enhancement and Convolutional Neural Network Xiaoli Sun, Jun Yang, Wei Huang, Shuai Teng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4759427/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract This paper proposes a novel crack detection method for concrete structures based on Fourier image enhancement and convolutional neural network (CNN). Cracking is regarded as an important indication for structure aging and durability decline, so the detection of cracks becomes more and more important. The CNN is used to detect the existence of cracks automatically. The original crack images may be disturbed by many factors, such as image blur, distortion and so on. In order to improve crack detection accuracy, it is necessary to pre-process the original images. This paper introduces a frequency-domain enhancement algorithm based on the Fourier transform, which is effective for crack detection from low-quality crack images (such as images with blurs and distortion). The results show that the testing accuracy of the crack images after Fourier enhancement reaches 100%. In this paper, a control experiment is designed to illustrate the effectiveness of this method, in which the crack images are not pre-processed and the testing accuracy is only 87.5%, and when the crack image is processed by median filter, and the testing accuracy is only 91.67%. The experimental results show that Fourier enhanced crack images can effectively improve the accuracy of crack detection and make the CNN training faster. Structural health monitoring Crack detection Convolutional neural network Fourier image enhancement Image processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Aug, 2024 Reviews received at journal 25 Aug, 2024 Reviews received at journal 23 Aug, 2024 Reviews received at journal 23 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers invited by journal 23 Jul, 2024 Editor assigned by journal 23 Jul, 2024 Submission checks completed at journal 23 Jul, 2024 First submitted to journal 17 Jul, 2024 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. 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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-4759427","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":341686774,"identity":"ab8126b5-1fc3-4c34-af7d-6a9f8fd8c6fb","order_by":0,"name":"Xiaoli Sun","email":"","orcid":"","institution":"Guangzhou Municipal Engineering Testing Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Sun","suffix":""},{"id":341686775,"identity":"7099479f-a668-4a0d-95a1-10b44d53b26e","order_by":1,"name":"Jun Yang","email":"","orcid":"","institution":"Guangzhou Municipal Engineering Testing Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yang","suffix":""},{"id":341686777,"identity":"7b087695-4aa2-4eab-a5cd-a08b49c5b56b","order_by":2,"name":"Wei Huang","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Huang","suffix":""},{"id":341686778,"identity":"39bd7a72-7d44-4de4-9585-12189b58b533","order_by":3,"name":"Shuai Teng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYNCCiho59gYQw8CCsGIeMHnmmDHPAbAWCSK1MLYxJ/aAtTAQocWevffwy59tbOk97D2mG34USDDwt3cn4LeF51yaNc85mdwenmNpN3uADpM4c3YDfi0SOWbGDGVsufslko/d4AFqMZDIJaBF/o2Z4Q825nQeicS2m3+I0iLBY/yAp405gQdoy23ibDmTY8bMc+aYIcgvt2UMJHgI+oW9/Yzxxx8VNfI87D1mN9/8sZHjb+/FrwUI2FDigoeQchBg/kCMqlEwCkbBKBjBAAB4VkGS1teGDQAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Teng","suffix":""}],"badges":[],"createdAt":"2024-07-18 02:40:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4759427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4759427/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62740329,"identity":"9e75e292-8f41-4e1d-856c-8dc484500f1d","added_by":"auto","created_at":"2024-08-19 02:37:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1020670,"visible":true,"origin":"","legend":"","description":"","filename":"reTeng240708.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4759427/v1_covered_7b55e09d-d775-4468-8d4a-bf3082623907.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Concrete Crack Detection based on Fourier Image Enhancement and Convolutional Neural Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Structural health monitoring, Crack detection, Convolutional neural network, Fourier image enhancement, Image processing","lastPublishedDoi":"10.21203/rs.3.rs-4759427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4759427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a novel crack detection method for concrete structures based on Fourier image enhancement and convolutional neural network (CNN). Cracking is regarded as an important indication for structure aging and durability decline, so the detection of cracks becomes more and more important. The CNN is used to detect the existence of cracks automatically. The original crack images may be disturbed by many factors, such as image blur, distortion and so on. In order to improve crack detection accuracy, it is necessary to pre-process the original images. This paper introduces a frequency-domain enhancement algorithm based on the Fourier transform, which is effective for crack detection from low-quality crack images (such as images with blurs and distortion). The results show that the testing accuracy of the crack images after Fourier enhancement reaches 100%. In this paper, a control experiment is designed to illustrate the effectiveness of this method, in which the crack images are not pre-processed and the testing accuracy is only 87.5%, and when the crack image is processed by median filter, and the testing accuracy is only 91.67%. The experimental results show that Fourier enhanced crack images can effectively improve the accuracy of crack detection and make the CNN training faster.\u003c/p\u003e","manuscriptTitle":"Research on Concrete Crack Detection based on Fourier Image Enhancement and Convolutional Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-19 02:29:47","doi":"10.21203/rs.3.rs-4759427/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-26T12:15:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-25T10:49:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-24T01:58:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T12:45:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221355427522994103263293081332899313061","date":"2024-08-22T05:37:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T00:20:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T20:24:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183641863896998205432359384960587621600","date":"2024-08-19T13:20:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46255341187640492708294168492172015482","date":"2024-08-16T23:54:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40381270606523840176492145012038766038","date":"2024-08-16T23:31:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93437199391901783001187297083813216855","date":"2024-08-16T12:27:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-29T05:58:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258317498843554910988386393735920844228","date":"2024-07-29T02:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T11:36:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-23T10:56:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-23T06:19:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Civil Engineering","date":"2024-07-18T02:38:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7799f70b-3d4d-41e1-b745-0a735bebcfaa","owner":[],"postedDate":"August 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T10:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-19 02:29:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4759427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4759427","identity":"rs-4759427","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}