YOLO-Defect: A New Multi-Scale YOLO-Based Deep Neural Network with Feature Enhancement for Multiclass Bridge 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 YOLO-Defect: A New Multi-Scale YOLO-Based Deep Neural Network with Feature Enhancement for Multiclass Bridge Surface Defect Detection Haihao Tang, Xiaobo Zhang, Yutao Liu, Donghai Zhai, Yongle Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6871824/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Bridges continuously face traffic pressure and environmental influences, leading to surface defects that need accurate detection for optimized maintenance and sustainability. The appearance and shape of bridge surface defects vary greatly, and multiple overlapping defects can occur in small areas. Current automatic detection methods also struggle with detection accuracy due to these complexities. Most studies focus on specific defect types and cannot fully assess a bridge's condition. We present YOLO-Defect, a YOLOv5-based multi-scale deep neural network designed to detect various complex bridge surface defects. It features two innovative modules: the global depthwise spatial pyramid pooling fusion module, which integrates multi-scale defect information, and the feature enhancement module, which enhances the differentiation between defects and background elements. YOLO-Defect is evaluated on the ZJU SYG crack and CODEBRIM datasets, outperforming benchmarks like Faster R-CNN, SSD, RetinaNet, and YOLOv5. On the ZJU SYG crack dataset, it achieves 78.2% [email protected] and 56.9% [email protected] :0.95, while on the CODEBRIM dataset, it achieves 39.5% [email protected] and 17.8% [email protected] :0.95. Multi-scale YOLO-Defect Deep neural network Feature enhancement Bridge defect detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 17 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 11 Jun, 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. 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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-6871824","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524535983,"identity":"de56e81a-9df7-4f59-892e-35b4794113be","order_by":0,"name":"Haihao Tang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Haihao","middleName":"","lastName":"Tang","suffix":""},{"id":524535984,"identity":"1f1f154a-f1f4-42e1-9e77-097815a1b64e","order_by":1,"name":"Xiaobo 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[email protected] and 56.9%
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[email protected] and 17.8%
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