Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection

Utilize Data Augmentation and Flexi Corner Block for Road Damage 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 Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection Zhaohui Wu, Zhaojia Li, XingLiang Sun, Runjing Zhao, Yingduo Bai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4570475/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 damage detection involves identifying cracks, potholes, and other surface irregularities from collected images. This technology is crucial for road maintenance and ensuring traffic safety. Despite significant progress in object detection algorithms, challenges such as weather-induced variability, dispersed key features, and diverse forms of damage persist. To address these issues, this paper proposes a road damage detection algorithm named Flexi-Weather Hard Detection, which integrates data augmentation based on AIGC and corner point feature aggregation. One of the modules, named Weather Trim Augment, utilizes stable diffusion technology to generate road damage data under various weather conditions. This enhancement expands the training dataset and reduces the negative impact of weather on detection accuracy. The Flexi Corner Block Block, utilizes deformable convolutions and combines a lightweight MLP with a learnable visual center mechanism to leverage corner points, enhancing local feature learning and improving the detection of subtle and dispersed features in a multi-scale context. Additionally, the HXIOU loss function is designed, employing weighted calculations and multiple metrics to effectively mine hard examples with significant variability, thus enhancing the detection accuracy of difficult cases such as blurred potholes and fine cracks. Comprehensive experiments on the RDD2020 and CNRDD datasets demonstrate that the proposed approach significantly improves performance, achieving 64.9% in the Test1 metric and 40.6% in the F1-Score. Notably, the algorithm achieves robust detection in unannotated, adverse weather conditions such as snow and rain, showcasing excellent eneralization capabilities. Road Damage Detection Data Augmentation Stable Diffusion Hard Example Mining Corner Point Detection 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-4570475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317094811,"identity":"3b8bcaf2-4d3d-41a0-ae09-b5126166bf27","order_by":0,"name":"Zhaohui Wu","email":"","orcid":"","institution":"China Academy of Transportation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Wu","suffix":""},{"id":317094812,"identity":"5a5a9504-7531-4b0a-b4a2-c78457bebf6f","order_by":1,"name":"Zhaojia Li","email":"","orcid":"","institution":"Capital Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhaojia","middleName":"","lastName":"Li","suffix":""},{"id":317094813,"identity":"3c7c3ef2-b909-4270-9397-a348dc954b8d","order_by":2,"name":"XingLiang Sun","email":"","orcid":"","institution":"Zhejiang Communications Investment Expressway Operation Management Co. 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This technology is crucial for road maintenance and ensuring traffic safety. Despite significant progress in object detection algorithms, challenges such as weather-induced variability, dispersed key features, and diverse forms of damage persist. To address these issues, this paper proposes a road damage detection algorithm named Flexi-Weather Hard Detection, which integrates data augmentation based on AIGC and corner point feature aggregation. One of the modules, named Weather Trim Augment, utilizes stable diffusion technology to generate road damage data under various weather conditions. This enhancement expands the training dataset and reduces the negative impact of weather on detection accuracy. The Flexi Corner Block Block, utilizes deformable convolutions and combines a lightweight MLP with a learnable visual center mechanism to leverage corner points, enhancing local feature learning and improving the detection of subtle and dispersed features in a multi-scale context. Additionally, the HXIOU loss function is designed, employing weighted calculations and multiple metrics to effectively mine hard examples with significant variability, thus enhancing the detection accuracy of difficult cases such as blurred potholes and fine cracks. Comprehensive experiments on the RDD2020 and CNRDD datasets demonstrate that the proposed approach significantly improves performance, achieving 64.9% in the Test1 metric and 40.6% in the F1-Score. 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