Steel Surface Defect Detection Based on Dynamic Receptive Field and Multi-Scale Features Fusion

Steel Surface Defect Detection Based on Dynamic Receptive Field and Multi-Scale Features Fusion | 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 Steel Surface Defect Detection Based on Dynamic Receptive Field and Multi-Scale Features Fusion Hongkai Zhang, Song Xue, Sixian Chan, Jianan Chen, Suqiang Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7718767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Steel surface defect detection is vital for guaranteeing product quality in contemporary manufacturing. However, traditional steel surface defect detection algorithms often face challenges due to insufficient resilience in feature extraction under complex backgrounds. To address this, we present a framework for defect detection which boosts feature extraction through a multi-path optimization strategy, markedly enhancing both accuracy and efficiency. Firstly, we introduce a dynamic receptive field (DRF) module which employs the spatial kernel selection mechanism to enable the network for dynamic perception according to defect scales. Meanwhile, a multi-scale feature fusion (MFF) module is designed to combine shallow and deep contextual information, minimizing information loss and enhancing feature representation. Finally, comprehensive experiments on the GC10-DET, NEU-DET, and APDDD datasets show that our model achieves a mean average precision of 71.4%, 82.0%, and 68.3%, respectively, outperforming state-of-the-art methods, while keeping efficient inference and minimal computational cost for real-time industrial applications. The source codes are at https://github.com/ssjddb/DM-YOLO.git. Steel surface defect detection Dynamic receptive field Multi-scale features fusion Lightweight network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 26 Sep, 2025 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-7718767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540924121,"identity":"8d51c47e-406f-4bb8-bd0e-93dcfba958cb","order_by":0,"name":"Hongkai Zhang","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Hongkai","middleName":"","lastName":"Zhang","suffix":""},{"id":540924122,"identity":"e555dc4d-b669-4f03-a3ec-f6ec946f6fc6","order_by":1,"name":"Song Xue","email":"","orcid":"","institution":"Anhui Jianzhu 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However, traditional steel surface defect detection algorithms often face challenges due to insufficient resilience in feature extraction under complex backgrounds. To address this, we present a framework for defect detection which boosts feature extraction through a multi-path optimization strategy, markedly enhancing both accuracy and efficiency. Firstly, we introduce a dynamic receptive field (DRF) module which employs the spatial kernel selection mechanism to enable the network for dynamic perception according to defect scales. Meanwhile, a multi-scale feature fusion (MFF) module is designed to combine shallow and deep contextual information, minimizing information loss and enhancing feature representation. 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