YOLO-FGA: A Lightweight yet High-Precision Network for Fine-Grained Anomaly Detection in Computer Chassis Assembly

YOLO-FGA: A Lightweight yet High-Precision Network for Fine-Grained Anomaly Detection in Computer Chassis Assembly | 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 YOLO-FGA: A Lightweight yet High-Precision Network for Fine-Grained Anomaly Detection in Computer Chassis Assembly Minming Gu, Xinyu Li, Shifei Hu, Zewei Mi, Nijie Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6826115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract The intelligent detection of computer chassis assembly states is a crucial for ensuring quality control and improving production efficiency in large-scale computer manufacturing processes. To overcome the limitations of traditional methods in dealing with complex internal backgrounds, subtle assembly differences, and frequent component occlusions, this paper proposes a lightweight detection framework, YOLO-FGA (You Only Look Once-Fine-Grained Anomaly), and presents an optimized design tailored to industrial computer vision applications. The model integrates the Re-parameterized Gradient Efficient Layer Aggregation Network (RepGELAN) in the YOLOv11 backbone structure, significantly enhancing the ability to extract features for subtle assembly differences. Additionally, a novel Contextual Anchor Attention Feature Pyramid Network (CASA-FPN) is introduced in the Neck structure, which resolves feature misalignment caused by occlusions and complex backgrounds via adaptive multi-scale fusion. Furthermore, a channel-wise knowledge distillation (CWKD) strategy is employed to enhance detection robustness while maintaining computational efficiency. Evaluation on a dataset containing 15 chassis components demonstrates that the YOLO-FGA model, after incorporating knowledge distillation, achieves significant performance improvements compared to YOLOv11: a 1.1% increase in mAP50, a 1.2% increase in mAP50:95, a 4.1% increase in accuracy, a 2% increase in F1 score, and a 30% reduction in number of parameters. These results demonstrate the potential and effectiveness of the method in high-precision and resource-efficient quality inspection systems for assembly states. YOLO-FGA Status of Computer Chassis Assembly RepGELAN CASA-FPN CWKD Full Text Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 16 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers invited by journal 06 Jun, 2025 Editor assigned by journal 06 Jun, 2025 First submitted to journal 04 Jun, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6826115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467422735,"identity":"e05f5e3e-7c2b-4af9-8c07-055db5c7d12c","order_by":0,"name":"Minming Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYJCCAwwMEgwM7A0ILpFaeA5AVROjBQIkEojUIh+RnXi4MMciTz7y+ePPH9sY5PhuJDB+LsCjxfBG7obDM7dJFBveTkiTONjGYCx5I4FZegY+LTOAWni3SSRunJ1wjAGoJXHDjQQ2Zh6itMw82PwBqKWeoBZ5CaiW+RLMDCCHJRgQ0mLA8xaiZQNPGpvEmXMShjPPPGyWxmtLe+7mz7zb6hLntx9//KGizEae73jywc94bTmAygDGKQNjAx4NQFsa0BmjYBSMglEwCtABADv+UtBwBnuoAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0484-3219","institution":"Zhejiang Sci-Tech University","correspondingAuthor":true,"prefix":"","firstName":"Minming","middleName":"","lastName":"Gu","suffix":""},{"id":467422736,"identity":"a11c9cd5-9af8-45f7-b952-aea950a0f413","order_by":1,"name":"Xinyu Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Li","suffix":""},{"id":467422737,"identity":"1eaf54da-19c7-4f77-b4c6-7122b00a9e69","order_by":2,"name":"Shifei Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shifei","middleName":"","lastName":"Hu","suffix":""},{"id":467422738,"identity":"cb361c71-0e95-4eda-8a34-661b950a505a","order_by":3,"name":"Zewei Mi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zewei","middleName":"","lastName":"Mi","suffix":""},{"id":467422739,"identity":"d6753c8b-a51b-4548-9381-e62824763f31","order_by":4,"name":"Nijie Shen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nijie","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-06-05 06:57:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6826115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6826115/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16057-x","type":"published","date":"2025-07-14T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87219390,"identity":"b02e48c9-caf6-4b00-beaa-243d7e068dcf","added_by":"auto","created_at":"2025-07-21 16:04:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":937574,"visible":true,"origin":"","legend":"","description":"","filename":"YOLOFGA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6826115/v1_covered_a5b6412e-6dcb-49c5-8f2f-939d35763686.pdf"}],"financialInterests":"","formattedTitle":"YOLO-FGA: A Lightweight yet High-Precision Network for Fine-Grained Anomaly Detection in Computer Chassis Assembly","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"YOLO-FGA, Status of Computer Chassis Assembly, RepGELAN, CASA-FPN, CWKD","lastPublishedDoi":"10.21203/rs.3.rs-6826115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6826115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe intelligent detection of computer chassis assembly states is a crucial for ensuring quality control and improving production efficiency in large-scale computer manufacturing processes. To overcome the limitations of traditional methods in dealing with complex internal backgrounds, subtle assembly differences, and frequent component occlusions, this paper proposes a lightweight detection framework, YOLO-FGA (You Only Look Once-Fine-Grained Anomaly), and presents an optimized design tailored to industrial computer vision applications. The model integrates the Re-parameterized Gradient Efficient Layer Aggregation Network (RepGELAN) in the YOLOv11 backbone structure, significantly enhancing the ability to extract features for subtle assembly differences. Additionally, a novel Contextual Anchor Attention Feature Pyramid Network (CASA-FPN) is introduced in the Neck structure, which resolves feature misalignment caused by occlusions and complex backgrounds via adaptive multi-scale fusion. Furthermore, a channel-wise knowledge distillation (CWKD) strategy is employed to enhance detection robustness while maintaining computational efficiency. 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