Enhanced Structural Anomaly Detection through Improved Image Inpainting and Feature-Level Discrimination

Enhanced Structural Anomaly Detection through Improved Image Inpainting and Feature-Level Discrimination | 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 Enhanced Structural Anomaly Detection through Improved Image Inpainting and Feature-Level Discrimination Dongce Fei, Qiliang Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8015767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In industrial production, the detection of structural anomalies on product surfaces is crucial for quality control. Traditional encoder-decoder architectures for unsupervised anomaly detection, while effective, tend to reduce the reconstruction error between normal and abnormal samples due to their generalization capabilities. To address this, we propose an enhanced structural anomaly detection algorithm based on improved image inpainting. By converting the reconstruction task into an inpainting-filling-reconstruction process, we amplify the reconstruction error between normal and abnormal samples. Our algorithm uses a feature loss and feature-level anomaly discrimination method to mitigate noise interference. Furthermore, we introduce a lightweight U-Net design to meet industrial requirements. Experimental results on the MVTec LOCO AD dataset demonstrate that our approach outperforms existing algorithms, achieving an average AUROC of 85.0% for anomaly detection and 88.5% for anomaly localization, highlighting its potential in industrial applications. Physical sciences/Engineering Physical sciences/Mathematics and computing Anomaly detection structural anomalies lightweight convolutional neural networks unsupervised learning image inpainting reconstruction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers invited by journal 27 Nov, 2025 Editor assigned by journal 17 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 06 Nov, 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. 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-8015767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":552803779,"identity":"becf508b-cec0-451f-92e2-7ff36c232d7f","order_by":0,"name":"Dongce Fei","email":"","orcid":"","institution":"Tiangong University","correspondingAuthor":false,"prefix":"","firstName":"Dongce","middleName":"","lastName":"Fei","suffix":""},{"id":552803780,"identity":"f5f28edb-a7ee-4287-abba-7ee63ab55398","order_by":1,"name":"Qiliang 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