Research on Ceramic Surface Micro Defect Detection Algorithm Based on RECS-DETR

Research on Ceramic Surface Micro Defect Detection Algorithm Based on RECS-DETR | 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 Research on Ceramic Surface Micro Defect Detection Algorithm Based on RECS-DETR Linyuxuan Li, Xinning Li, Mingda Huang, Xianhai Yang, Hu Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6299544/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 In the task of surface defect detection for ceramic materials, identifying minute defects has always posed a challenge due to the difficulty of balancing detection accuracy with computational cost. To address this issue, this paper proposes an enhanced algorithm based on RT-DETR, named RECS-DETR (RDNet-EA Attention-CARAFE-SCFF-RTDETR). First, a lightweight RD feature extraction network is innovatively designed to replace the backbone network of RT-DETR, and Efficient Additive Attention is introduced to substitute the self-attention mechanism in AIFI, thereby reducing computational costs. Next, the CARAFA upsampling module is employed to replace the Upsample module in RT-DETR, which better aggregates information and captures features. Subsequently, a novel structure called SCFF is designed specifically for tiny object detection. The SCFF small object detection optimization module introduces the S2 layer as the small object detection layer and constructs a new feature fusion pyramid, further enhancing the model's sensitivity to minute defects. Finally, a novel composite loss optimization strategy, NWD-Inner-Wise-IoU, is implemented, significantly accelerating model convergence and enhancing the detection of small objects. Experimental results demonstrate that, compared to the ResNet18-based RT-DETR algorithm, the proposed RECS-DETR achieves a 4.4 percentage point improvement in mean average precision on a ceramic surface dataset characterized by tiny defects with black spots. Additionally, it reduces the number of parameters by 45%, decreases FLOPs by 35.8%, and increases detection speed by 17%. The significantly improved performance effectively meets the requirements for detecting minute defects on ceramic surfaces in industrial production. Ceramic surface defect detection Small target detection Lightweight RT-DETR loss function Full Text 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-6299544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436684015,"identity":"2134741e-997b-4f8c-91a6-377fbd3cb5a0","order_by":0,"name":"Linyuxuan Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Linyuxuan","middleName":"","lastName":"Li","suffix":""},{"id":436684016,"identity":"ac81b57c-b34b-4c9f-b817-cf75ae8ecb68","order_by":1,"name":"Xinning Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinning","middleName":"","lastName":"Li","suffix":""},{"id":436684017,"identity":"6152a422-df9c-4c58-b8fa-20ea444389da","order_by":2,"name":"Mingda Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mingda","middleName":"","lastName":"Huang","suffix":""},{"id":436684018,"identity":"496f6875-8dcf-47b9-ae12-a2ecdec4f6d7","order_by":3,"name":"Xianhai Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xianhai","middleName":"","lastName":"Yang","suffix":""},{"id":436684019,"identity":"40c85415-812a-463c-a46d-9486b6ff7421","order_by":4,"name":"Hu Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFACHiD+YSPHz8x88AHxWhh70owl29mSDUiwhe1w4obzPGYCRGkwuJF7TIKBh5lx82EGMwaGGptoIrTkpUkwWLAxmx1mSHvAcCwtt4GQFrMbOWZAW3jYgFqOGzA2HCZWC5sEj3EzY5sEKVoMJAyYmdmI02J/5o2xBWNPgoHEYTZmgwRi/CLZnmN4g+HH//r+/vMfH3yosSGsBQSY/8BYCcQoHwWjYBSMglFAGAAAEGo3meUkAEYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2032-3176","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hu","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-25 03:20:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6299544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6299544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83916566,"identity":"f4706cda-de40-49da-b637-d800b7ae6034","added_by":"auto","created_at":"2025-06-04 12:52:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":844008,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscirpt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6299544/v1_covered_b0a3b954-230a-4992-955c-67ff884113f6.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eResearch on Ceramic Surface Micro Defect Detection Algorithm Based on RECS-DETR\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ceramic surface defect detection, Small target detection, Lightweight, RT-DETR, loss function","lastPublishedDoi":"10.21203/rs.3.rs-6299544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6299544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the task of surface defect detection for ceramic materials, identifying minute defects has always posed a challenge due to the difficulty of balancing detection accuracy with computational cost. To address this issue, this paper proposes an enhanced algorithm based on RT-DETR, named RECS-DETR (RDNet-EA Attention-CARAFE-SCFF-RTDETR). First, a lightweight RD feature extraction network is innovatively designed to replace the backbone network of RT-DETR, and Efficient Additive Attention is introduced to substitute the self-attention mechanism in AIFI, thereby reducing computational costs. Next, the CARAFA upsampling module is employed to replace the Upsample module in RT-DETR, which better aggregates information and captures features. Subsequently, a novel structure called SCFF is designed specifically for tiny object detection. The SCFF small object detection optimization module introduces the S2 layer as the small object detection layer and constructs a new feature fusion pyramid, further enhancing the model's sensitivity to minute defects. 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