DCUE-YOLO: A Lightweight Model in Industrial Defect Detection

DCUE-YOLO: A Lightweight Model in Industrial 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 Research Article DCUE-YOLO: A Lightweight Model in Industrial Defect Detection Jiajin Zhong, HongCheng Wang, JiaLin Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5790775/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 Accurate and rapid identification of defects in industrial products is essential for ensuring quality and safety. However, the challenges presented by large-scale production environments, along with the difficulty in distinguishing between target defects and complex backgrounds, complicate defect detection. Consequently, most target detection models struggle to achieve an optimal balance between detection accuracy and efficiency. To improve detection accuracy and efficiency, this paper proposes a lightweight network architecture, DCUE-YOLO, based on YOLOv10. The primary objective is to improve both the accuracy and efficiency of industrial product defect detection. In addition, a feature extraction module with double convolutional path design with hidden channels is proposed under the premise of reducing the computational complexity; by capturing information of different scales, the model can enhance the ability to distinguish small target defects from complex backgrounds. In order to further improve the model's attention to small target defects, this paper also proposes a multifilter attention mechanism design. Meanwhile, in order to effectively solve the problem of partial feature information loss in the process of downsampling, this paper also uses a transposed convolution design. Extensive experiments were carried out using PCB, NEU-DET and mixed-type WM38 public data sets, producing mean mean average precision (mAP) scores of 94.3%, 90.5%, and 98.7%, respectively. Compared to the YOLOv10s model, our mAP has improved by 2.7%, 1.8%, and 1.2%, respectively, while the parameter count has decreased by 0.3M. Our model demonstrates advantages in recognition accuracy and inference speed, thus validating its effectiveness for industrial defect detection. Industrial defect detection DCUE-YOLO light-weight neural network deep learning 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-5790775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401041125,"identity":"02f25710-fab1-4947-a11d-50824991d4b9","order_by":0,"name":"Jiajin Zhong","email":"","orcid":"","institution":"School of Computer Science and Technology, Dongguan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiajin","middleName":"","lastName":"Zhong","suffix":""},{"id":401041127,"identity":"bd6b5175-efb1-4065-9f96-a0500595cbdf","order_by":1,"name":"HongCheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZgYGG8YGMJONeC1pQC3MpGk5TIIWg+NnD78uqDkvOz8i/wDDh7LDDPyzGwhoOZOXZj3j2G3jjTeSGRhnnDvMIHHnAH4tZgdyzIx5G24nbpyRzMDM23aYwUAigYCW829AWs5BtPwlSsuNHOPHvA0HEudLALUwEqPF/sYbM2aeY8nGG3geGxzsOZfOI3GDgBbJ/hzjzzw1drLz2xMfPvhRZi3HP4OAFiBgkwCRBgcYGICIgYegeiBg/gAi5RuIUTsKRsEoGAUjEgAA29tFPyYxBSwAAAAASUVORK5CYII=","orcid":"","institution":"School of Electrical Engineering and Intelligentization, Dongguan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"HongCheng","middleName":"","lastName":"Wang","suffix":""},{"id":401041130,"identity":"c6cf1165-eed6-4252-9499-68471d927b2d","order_by":2,"name":"JiaLin Zou","email":"","orcid":"","institution":"School of Computer Science and Technology, Dongguan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"JiaLin","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2025-01-08 16:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5790775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5790775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76412230,"identity":"91dcd8a1-30c0-4bed-a56d-c65e7282ca8b","added_by":"auto","created_at":"2025-02-17 01:31:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2551859,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5790775/v1_covered_deca6ba3-b2a2-4b84-b239-4610f73fa70f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DCUE-YOLO: A Lightweight Model in Industrial Defect Detection","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":false,"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":"Industrial defect detection, DCUE-YOLO, light-weight neural network, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5790775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5790775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate and rapid identification of defects in industrial products is essential for ensuring quality and safety. 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