{"paper_id":"48556f86-ecc2-49c6-babe-d24d24397a44","body_text":"Human-Machine Collaborative Enhanced Interpretable Distillation Model for High-Precision Online Defect Detection | 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 Human-Machine Collaborative Enhanced Interpretable Distillation Model for High-Precision Online Defect Detection Shuxuan Zhao, Yunqing Tang, Hongwei Xu, Lilan Liu, Wei Qin, Jie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7043856/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 Online vision-based defect detection is highly preferred in smart manufacturing for its ability to provide immediate feedback and enable timely correction. However, effective human-machine collaboration in practical deployment faces significant challenges: existing models often lack interpretability, hindering operators from understanding the rationale behind model decisions, effectively intervening in critical judgments, or optimizing the process, thus limiting the system's reliability and efficiency. Concurrently, online detection imposes stringent demands on real-time performance. To address these dual challenges, this research proposes a Human-Machine Collaborative Enhanced Interpretable Knowledge Distillation strategy. It aims to boost the real-time performance of detection models while guaranteeing high accuracy and, crucially, interpretability, thereby effectively supporting human-machine collaboration. Firstly, a CNN-Transformer hybrid network is designed, leveraging the strengths of self-attention for global receptive fields and convolution operations for local receptive fields, to robustly extract features of tiny and irregularly shaped defects. Secondly, an innovative explainable knowledge quantization method is devised to quantize defect and texture features into interpretable knowledge units, explicitly characterizing the model's capability in feature extraction and providing a transparent basis for human interaction. Finally, an explainable knowledge alignment loss function is proposed. It utilizes the superior defect feature extraction capability of the teacher model as a key learning objective for the student model, enabling the student to achieve more precise defect detection with a simpler network architecture. Experimental results demonstrate that the proposed CNN-Transformer hybrid network achieves over 95% accuracy and recall. Visualization experiments confirm that the method better focuses on defect features. More importantly, the explainable knowledge distillation strategy significantly outperforms other lightweight methods. It not only satisfies the stringent accuracy and real-time requirements of online defect detection but, critically, its inherent interpretability directly empowers human-machine collaboration. This allows operators to comprehend, trust, and effectively utilize the model's outputs, collaboratively enhancing the overall performance of the detection system. Physical sciences/Engineering Physical sciences/Mathematics and computing Online defect detection CNN-Transformer hybrid network Explainable knowledge distillation Human-machine collaboration 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-7043856\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":490012061,\"identity\":\"1d7d4ebd-eb13-44f8-bda7-905abc23698d\",\"order_by\":0,\"name\":\"Shuxuan Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The University of Hong Kong\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shuxuan\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":490012062,\"identity\":\"41bd1d83-ac39-447f-9fc8-80af8522f0ee\",\"order_by\":1,\"name\":\"Yunqing 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