A Lightweight Network FLA-Detect for Steel Surface Defect Detection

A Lightweight Network FLA-Detect for Steel Surface Defect Detection | 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 Lightweight Network FLA-Detect for Steel Surface Defect Detection Jingzhe Wang, Yue Wang, Aixi Sun, Yu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4581669/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 Rapid and accurate defect detection is crucial for enhancing product quality in the steel industry. This paper proposes a steel surface defect detection network, FLA-Detect, to address these challenges. Three key improvements are integrated into FLA-Detect. To achieve a lightweight model, a new structure called FC2f has been introduced, significantly reducing the model's parameters Additionally, a lightweight attention mechanism named Large Separable Kernel Attention (LSKA) is incorporated, enhancing the accuracy of small target defect detection and improving network robustness. To further enhance feature extraction and model deployment feasibility, a new downsampling structure called AMDown is designed. The performance of FLA-Detect is validated through comparative ablation experiments on the NEU-DET dataset, followed by generalization validation on the GC10 dataset. These experimental results demonstrate the effectiveness and accuracy of FLA-Detect. Defect detection Deep learning Lightweight network Steel Full Text Additional Declarations No competing interests reported. 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-4581669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318960901,"identity":"c484d1bd-6534-49b7-8d65-3b99bf70b8b8","order_by":0,"name":"Jingzhe Wang","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhe","middleName":"","lastName":"Wang","suffix":""},{"id":318960902,"identity":"ebec06fb-82a3-4042-bae0-8c40441ed02b","order_by":1,"name":"Yue Wang","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":318960904,"identity":"68212c97-45f6-4fb7-83db-352e85c57c52","order_by":2,"name":"Aixi Sun","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Aixi","middleName":"","lastName":"Sun","suffix":""},{"id":318960905,"identity":"9e284e1a-8aee-456c-a3d2-b830423065c1","order_by":3,"name":"Yu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYFCCAyDChoGxgUQtaSRpAYPDJKiVbzx7TPJn23l75tk9Bgw/dxChhbHhXJqEZNttZsY5ZwwYe88QoYWZ4YyZhGHbbTbGGTkGzIxtRGhhA2lJbDvHQ7wWHpCWg20HJIjXIsFwxtiy4VyyAeOMtIKDvcRokZ9xxvDmjzI7e8MZyRsf/CRGC4PEAWCwsTEwGDZAY5Uw4AcqZfgDtI445aNgFIyCUTASAQCYGTND3QMiaAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-14 11:14:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4581669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4581669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100547676,"identity":"d7f64952-f581-4a27-888a-5a85c072fe79","added_by":"auto","created_at":"2026-01-19 08:16:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910263,"visible":true,"origin":"","legend":"","description":"","filename":"ALightweightNetworkFLADetectforSteelSurfaceDefectDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4581669/v1_covered_1797f13f-92da-4739-940d-fe73b2cefe56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Lightweight Network FLA-Detect for Steel Surface Defect Detection","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":false,"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":"Defect detection, Deep learning, Lightweight network, Steel","lastPublishedDoi":"10.21203/rs.3.rs-4581669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4581669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rapid and accurate defect detection is crucial for enhancing product quality in the steel industry. 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