Lightweight Yet Precized: A Redesigned YOLO for High-Accuracy Road Crack Detection on Vehicle-Mounted Devices

Lightweight Yet Precized: A Redesigned YOLO for High-Accuracy Road Crack Detection on Vehicle-Mounted Devices | 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 Lightweight Yet Precized: A Redesigned YOLO for High-Accuracy Road Crack Detection on Vehicle-Mounted Devices Chongyu Luo, Ji Li, Xuan Song, Yabing Liu, Wentao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7735353/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 Road crack detection plays a critical role in ensuring traffic safety and enabling timely maintenance. However, real-time detection algorithms deployed on vehicle-mounted platforms often struggle with several challenges. These include poor sensitivity to small cracks, incomplete feature extraction, and difficulty balancing detection accuracy with model efficiency. This study presents LP-YOLOv8, an enhanced object detection algorithm based on an improved YOLOv8n architecture. The proposed method is designed to offer a lightweight yet precise solution for road crack detection. To reduce model complexity while maintaining high accuracy, we introduce the C2f-faster module, which optimizes the backbone network by minimizing parameter redundancy and suppressing background noise. We further propose a Lightweight Shared Detail-Enhanced Convolution Detection Head (LSDECD) to enhance multi-scale feature perception while minimizing parameter count. To improve the detection of small cracks, we redesign the neck using a Focal Diffusion Pyramid Network (FDPN). FDPN strengthens cross-scale feature fusion by regulating deep-to-shallow feature interactions through learnable attention gating. Moreover, we propose an improved Inner-WIoU loss function that focuses on small objects and samples with general quality labels. The LP-YOLOv8 method was evaluated on the RDD2022 vehicle-mounted image dataset through extensive comparative and ablation studies. Experimental results demonstrate a 3.41% improvement in [email protected] over YOLOv8n, while reducing the number of parameters by 55.6% and computational costs by 36.7%. These findings confirm that LP-YOLOv8 provides an efficient and accurate solution for real-time road crack detection in vehicle-mounted imaging systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Road surface crack detection Vehicle-mounted images Lightweight design Precise design LP_YOLOv8 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-7735353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":530873230,"identity":"36c532d1-a53e-4a05-8d53-6211482820b1","order_by":0,"name":"Chongyu Luo","email":"","orcid":"","institution":"Power China Chengdu Engineering Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Chongyu","middleName":"","lastName":"Luo","suffix":""},{"id":530873231,"identity":"9b30e1d3-b773-4b68-bc0f-e0fc19d5bdc2","order_by":1,"name":"Ji Li","email":"","orcid":"","institution":"Hunan Agricultural 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