Improved YOLOv7-Based Algorithm for Detecting Defect on Steel Surface

Improved YOLOv7-Based Algorithm for Detecting Defect on Steel Surface | 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 Improved YOLOv7-Based Algorithm for Detecting Defect on Steel Surface Cong Hu, Danni Gong, Tian Zhou, Tianhao Huang, Xijun Huang, Chunting Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4680873/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 Steel in the production and processing process is easily affected by raw materials and processing environment, resulting in surface defects, affecting its corrosion resistance and service life. In addition, the defect detection model based on deep learning is relatively large, and it is difficult to meet the demand for real-time steel surface defect detection tasks. To address the above problems, this paper proposes an improved model YOLOv7-Lig based on the YOLOv7-tiny model. by introducing the CBAM attention mechanism, as well as using the Wise-IoU loss function to replace the original CIoU loss function, the network model is enhanced to pay attention to the key features of the input data, so that it can accurately capture the true boundary of the target, effectively reduces the problem of inadequate feature extraction. The CSP-Faster module is introduced in the BACKBONE, which reduces the number of parameters of the net work model and helps the network model to be deployed in resource-constrained environments. Then the CARAFE upsampling operator is introduced at the head, which effectively aggregates the context information and improves the accuracy of the upsampling. The YOLOv7-Lig model achieves a mAP of 85.1% and a detection speed of 82FPS, which proves that the model can improve the accuracy and detection speed of the model while reducing the number of parameters. Steel surface defects Deep learning Target detection YOLOv7-tiny 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-4680873","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330471679,"identity":"e2e328a4-38cc-4248-a7be-0733159dd31f","order_by":0,"name":"Cong Hu","email":"","orcid":"","institution":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Hu","suffix":""},{"id":330471680,"identity":"8d0e7bd7-7650-49de-9c5b-ebe9a94376be","order_by":1,"name":"Danni Gong","email":"","orcid":"","institution":"School of 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In addition, the defect detection model based on deep learning is relatively large, and it is difficult to meet the demand for real-time steel surface defect detection tasks. To address the above problems, this paper proposes an improved model YOLOv7-Lig based on the YOLOv7-tiny model. by introducing the CBAM attention mechanism, as well as using the Wise-IoU loss function to replace the original CIoU loss function, the network model is enhanced to pay attention to the key features of the input data, so that it can accurately capture the true boundary of the target, effectively reduces the problem of inadequate feature extraction. The CSP-Faster module is introduced in the BACKBONE, which reduces the number of parameters of the net work model and helps the network model to be deployed in resource-constrained environments. Then the CARAFE upsampling operator is introduced at the head, which effectively aggregates the context information and improves the accuracy of the upsampling. 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