Research on Metal Surface Defect Detection Method Based on Deep Learning

Research on Metal Surface Defect Detection Method Based on Deep Learning | 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 Article Research on Metal Surface Defect Detection Method Based on Deep Learning Yuqin Feng, Geng Sun, Yawei Zhao, Fu Wang, Xunran Yu, Xinyue Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7831522/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract In deep learning single-stage object detection algorithms, the YOLO series has emerged as a prominent focus in the object detection domain due to its streamlined architecture, high detection efficiency, and excellent accuracy. The YOLOv8 variant, in particular, demonstrates superior overall performance in terms of real-time processing and precision, making it an ideal algorithm for defect detection tasks. To address the challenges in metal surface defect detection, we developed CDA-YOLOv8, an enhanced architecture based on the YOLOv8s framework. The proposed enhancements encompass three critical components: the feature extraction network, the feature fusion module, and the detection head network. Through this series of optimizations, the model's effectiveness in detecting metal surface defects is enhanced, thereby providing technical support for the application of deep learning in practical industrial production scenarios. Physical sciences/Engineering Physical sciences/Mathematics and computing metal surface defect detection object detection deep learning CDA-YOLOv8 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor invited by journal 14 Oct, 2025 Editor assigned by journal 11 Oct, 2025 Submission checks completed at journal 11 Oct, 2025 First submitted to journal 10 Oct, 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-7831522","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534671907,"identity":"2733c01f-1c49-412d-b519-17d3b0384da5","order_by":0,"name":"Yuqin Feng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuqin","middleName":"","lastName":"Feng","suffix":""},{"id":534671908,"identity":"34748f0a-7d77-477b-9253-5de10f42369e","order_by":1,"name":"Geng 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