DSC-YOLO: Non-Destructive Defect Detection of Tire X-Ray Images Based on Dynamic Snake Convolution

DSC-YOLO: Non-Destructive Defect Detection of Tire X-Ray Images Based on Dynamic Snake Convolution | 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 DSC-YOLO: Non-Destructive Defect Detection of Tire X-Ray Images Based on Dynamic Snake Convolution Guangpeng Xu, Aijuan Li, Xibo Wang, Chuanyan Xu, Jiaqi Chen, Fei Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4610707/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Tire X-ray nondestructive testing before leaving the factory is crucial for driving safety. Given the complexity of tire structures and the diversity of defect types, traditional manual visual inspections and machine learning methods face significant challenges in terms of accuracy and efficiency. This study proposes an innovative tire X-ray image nondestructive testing technique based on the YOLOv5 model, incorporating several advanced technologies to enhance detection performance. Specifically, we introduce Dynamic Snake Convolution (DSConv), which adaptively focuses on slender and curved features within tires. Additionally, we have designed a C3 module based on DSConv, specifically targeting slender defects such as cord-overlap and cord-cracking. To improve the detection accuracy of small defects, we redesigned the neck network structure and introduced the Scale sequence feature fusion module (SSFF) and the Triple feature encoding module (TFE) to integrate multi-scale information from different network layers. Furthermore, we developed the Convolution Block Attention Module, integrated into the SSFF, which effectively reduces the interference of complex backgrounds and focuses on defect recognition. In the post-processing stage, we employed the Soft-NMS algorithm to optimize the confidence of candidate detection boxes, enhancing the accuracy of box selection. The experimental results show that compared to the YOLOv5 benchmark model, the algorithm proposed in this study achieved a 5.9 percentage point increase in mAP 0.5 and a 5.7 percentage point increase in mAP 0.5:0.95 , demonstrating superior detection accuracy compared to current mainstream object detection algorithms and effectively completing the nondestructive testing task of tire defects. Physical sciences/Engineering/Mechanical engineering Physical sciences/Engineering Physical sciences/Mathematics and computing tire defect detection dynamic snake convolution YOLOv5 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviews received at journal 29 Sep, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers invited by journal 17 Sep, 2024 Editor assigned by journal 10 Sep, 2024 Editor invited by journal 24 Jun, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 20 Jun, 2024 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-4610707","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326619483,"identity":"7f290d57-3d80-4a29-b61a-065dcfc9a6ba","order_by":0,"name":"Guangpeng Xu","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Guangpeng","middleName":"","lastName":"Xu","suffix":""},{"id":326619484,"identity":"23a097f2-e5b8-467e-98ac-82b605fb7171","order_by":1,"name":"Aijuan 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Given the complexity of tire structures and the diversity of defect types, traditional manual visual inspections and machine learning methods face significant challenges in terms of accuracy and efficiency. This study proposes an innovative tire X-ray image nondestructive testing technique based on the YOLOv5 model, incorporating several advanced technologies to enhance detection performance. Specifically, we introduce Dynamic Snake Convolution (DSConv), which adaptively focuses on slender and curved features within tires. Additionally, we have designed a C3 module based on DSConv, specifically targeting slender defects such as cord-overlap and cord-cracking. To improve the detection accuracy of small defects, we redesigned the neck network structure and introduced the Scale sequence feature fusion module (SSFF) and the Triple feature encoding module (TFE) to integrate multi-scale information from different network layers. 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