Automatic and accurate determination of defect size in shearography using U-Net deep learning network

Automatic and accurate determination of defect size in shearography using U-Net deep learning 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 Research Article Automatic and accurate determination of defect size in shearography using U-Net deep learning network Rong Wu, HaiBo Wei, Chao Lu, Yuan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5156086/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Dec, 2024 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 9 You are reading this latest preprint version Abstract Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection. Shearography Defect size measurement System calibration Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Dec, 2024 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 13 Nov, 2024 Reviews received at journal 10 Nov, 2024 Reviews received at journal 03 Nov, 2024 Reviewers agreed at journal 23 Oct, 2024 Reviewers agreed at journal 23 Oct, 2024 Reviewers invited by journal 09 Oct, 2024 Editor assigned by journal 08 Oct, 2024 Submission checks completed at journal 28 Sep, 2024 First submitted to journal 26 Sep, 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. 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. 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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-5156086","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377537421,"identity":"82c02610-e4be-4c0c-a942-3e48ed847650","order_by":0,"name":"Rong Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYPACGwYGCRDNRryWNNK1HCZBi3l77+HXvG3n8/hn9xgwfCg7zMA/uwG/Fpkz59KsedtuF0vcOWPAOOPcYQaJOwfwa5GQyDEzzm27nbhBIseAmbftMIOBRAJRWs5BtPwlUovx49y2AxAtjERp4TljxvznXHLijBtpBQd7zqXzSNwgpIW9x/jjjDK7xP4ZyRsf/CizluOfQUALELBJwFgHgJiHoHogYP5AjKpRMApGwSgYwQAA6GA/mcvRALQAAAAASUVORK5CYII=","orcid":"","institution":"Nanchang Hangkong University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wu","suffix":""},{"id":377537422,"identity":"37c3b6ff-0962-4d0b-98e7-5d1571072b3d","order_by":1,"name":"HaiBo Wei","email":"","orcid":"","institution":"Nanchang Hangkong University","correspondingAuthor":false,"prefix":"","firstName":"HaiBo","middleName":"","lastName":"Wei","suffix":""},{"id":377537423,"identity":"7a4a19d9-811b-445e-8628-0cde0606743c","order_by":2,"name":"Chao Lu","email":"","orcid":"","institution":"Nanchang Hangkong University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Lu","suffix":""},{"id":377537424,"identity":"36f1f4df-2a20-4f90-8a4c-eed5c9f9c627","order_by":3,"name":"Yuan Liu","email":"","orcid":"","institution":"Nanchang Hangkong University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-26 06:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5156086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5156086/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-024-01149-7","type":"published","date":"2024-12-25T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72640814,"identity":"a90e9479-e11b-4472-876b-0176880ddffb","added_by":"auto","created_at":"2024-12-30 16:09:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":943104,"visible":true,"origin":"","legend":"","description":"","filename":"JNEmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5156086/v1_covered_1fab74ec-cca0-4446-89c7-feaef266c53a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automatic and accurate determination of defect size in shearography using U-Net deep learning 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":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Shearography, Defect size measurement, System calibration, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5156086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5156086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. 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