Aircraft skin damage detection method integrating multi-scale feature reconstruction and adaptive loss weight

Aircraft skin damage detection method integrating multi-scale feature reconstruction and adaptive loss weight | 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 Aircraft skin damage detection method integrating multi-scale feature reconstruction and adaptive loss weight Jun Wu, Yajing Zhang, Zhiwei Xing, Jiusheng Chen, Runxia Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6834553/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 Aircraft skin damage poses a significant threat to the safety and airworthiness of aircraft. The considerable variation in the size of damage objects introduces challenges in feature extraction and compromises the accuracy of detection models. To address these issues, this paper proposes an improved aircraft skin damage detection algorithm based on YOLOv9s. In the feature extraction stage, a lightweight Inception module is introduced to enhance the representation of multi-scale features while reducing the model's parameter count. In the feature fusion stage, the SKFusion module, incorporating the SKNet attention mechanism, is introduced to further improve the model's ability to capture damage features at different scales. Additionally, a dynamic weighted optimization strategy combining an improved Focaler loss and inner-DIoU loss function is employed to enhance the recognition of hard-to-classify samples. The proposed method was evaluated on both a self-constructed aircraft skin damage dataset and the publicly available NEU-DET dataset. Experimental results demonstrate that, compared with the original YOLOv9s, the improved model achieves a 1.6% and 3.9% increase in mAP@50 on the self-constructed and NEU-DET datasets, respectively, while reducing the model's parameters by 16.7%. Aircraft skin damage Multi-scale damage Object detection YOLOv9 Feature fusion 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-6834553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544757866,"identity":"a66e95ce-a481-42ea-a79a-c82b743171a9","order_by":0,"name":"Jun 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