AWBN-YOLO: A Surface Defect Detection Method for Aero-Engine Blades in Sample-Limited Scenarios

AWBN-YOLO: A Surface Defect Detection Method for Aero-Engine Blades in Sample-Limited Scenarios | 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 AWBN-YOLO: A Surface Defect Detection Method for Aero-Engine Blades in Sample-Limited Scenarios Weixuan Gao, Nengbin Lv, Fuzhou Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6416020/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Multimedia Systems → Version 1 posted 12 You are reading this latest preprint version Abstract In the production process of aero-engine blades (AEBs), surface defect detection is essential. However, data scarcity and class imbalance in practical industrial scenarios make deep learning-based defect identification of AEBs challenging. In this article, we propose AWBN-YOLO, an enhanced YOLOv10n-based framework to address these challenges. Specifically, we design an Adaptive Sample Augmentation Method (ASAM) to synthesize photorealistic defect samples by adaptively aligning defect geometries with blade contours and optimizing background consistency. We also propose a Feature-driven Wavelet Downsampling (FWD) module to preserve critical spatial-frequency details through adaptive wavelet basis selection, enhancing sensitivity to fine-grained defects. Furthmore, we introduce BiFPN-Concat and Normalized Wasserstein Distance Loss (NWD-Loss) to optimize multi-scale feature fusion and small-defect localization. Experiments on the AeBAD-SL dataset, a sample-imbalanced benchmark for AEBs have proven that AWBN-YOLO can achieve state-of-the-art performance with 82.2% precision, 71.9% recall, and 71.7% mAP50, surpassing the baseline YOLOv10n by 2.8%, 1.2%, and 2.6%, respectively. AWBN-YOLO achieves superior detection accuracy while maintaining real-time performance (140 FPS), offering a robust solution for industrial quality inspection under practical constraints. Surface defect detection Data augmentation YOLO optimization Aero-engine blades Industrial inspection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Multimedia Systems → Version 1 posted Editorial decision: Revision requested 09 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 05 May, 2025 Editor assigned by journal 04 May, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 09 Apr, 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-6416020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452416068,"identity":"ab7b0b6a-bb3b-465c-9add-cc760c61f0c1","order_by":0,"name":"Weixuan Gao","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Weixuan","middleName":"","lastName":"Gao","suffix":""},{"id":452416069,"identity":"3accf2cc-4dd4-43cf-a722-cb8e4d514ba6","order_by":1,"name":"Nengbin Lv","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Nengbin","middleName":"","lastName":"Lv","suffix":""},{"id":452416071,"identity":"c0ec133b-63d6-4a19-ad0a-f314ee68cec0","order_by":2,"name":"Fuzhou Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDCCAwwMH8Ake2Pjww9EamGcAdbCc7jZWII0LRLpbQI8xOjgu5HA2Mzz546cwc2HbQwSDHZyug0EtEiCtfA8Mza4ndj2oIAh2djsAAEtBjcS2B/zSBxO3HA7sd1AguFA4jYitABtMQBquXmwTYKHeC0JQC03GInUInnmAWPjnAOHjSXPJAID2YAIv/AdT2BsePPnsBzf8eMPH36osJMjqIWBgR85yg0IKh8Fo2AUjIJRQAwAAExHSaSqQwCeAAAAAElFTkSuQmCC","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Fuzhou","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-04-10 03:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6416020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6416020/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00530-025-01967-3","type":"published","date":"2025-08-21T16:29:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847400,"identity":"d8d8bc16-b84a-401b-9c6c-3a4d6e724a10","added_by":"auto","created_at":"2025-08-25 16:43:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1290211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6416020/v1_covered_8fa8431b-6f6e-4617-9b03-00a070575634.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AWBN-YOLO: A Surface Defect Detection Method for Aero-Engine Blades in Sample-Limited Scenarios","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Surface defect detection, Data augmentation, YOLO optimization, Aero-engine blades, Industrial inspection","lastPublishedDoi":"10.21203/rs.3.rs-6416020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6416020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the production process of aero-engine blades (AEBs), surface defect detection is essential. 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