An Improved and Efficient PSMD-Yolo Algorithm for Detecting Surface Defects in Fabrics

An Improved and Efficient PSMD-Yolo Algorithm for Detecting Surface Defects in Fabrics | 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 An Improved and Efficient PSMD-Yolo Algorithm for Detecting Surface Defects in Fabrics Cunli Song, Yueling Yan, Weiguo Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703117/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 Addressing the issue of poor accuracy in traditional detection methods for fabrics with significantly varying textures and small defect targets, this paper proposes an improved detection algorithm, PSMD-Yolo, based on YOLOv8. Firstly, a lightweight convolution module, PP-HGNet, enhancing the network's ability to process complex image data. This is combined with the S2-MLP attention mechanism to fuse the segmented feature maps, thereby improving image recognition accuracy. Secondly, MAFPN is added to the Neck layer to increase the efficiency of feature fusion . Finally, the MPD-IOU loss function is introduced, which considers the distance between center points to achieve more accurate regression results. Experiments were conducted on a fabric defect dataset collected from a textile factory in Zhejiang. Compared to the original YOLOv8 model, the P-value increased by 1.4%, the R-value by 2.7%, mAP50 by 1.4%, and mAP50:95 by 2.8%. The detection P-value and mAP50 accuracy reached 96.5% and 98.1%, respectively. The average precision improved by 6.5, 4.8, and 1.4 percentage points relative to SSD, Faster-RCNN, and Centernet, respectively.To further verify its generalization, the algorithm was evaluated on the Alibaba Cloud Tianchi fabric defect dataset, resulting in an average precision increase of 3 percentage points. Fabric Defects Lightweight Convolution Feature Fusion Loss Function 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. 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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-6703117","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463736577,"identity":"549016d1-ffa1-4546-ac4c-9213de535fdb","order_by":0,"name":"Cunli Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBAC+QYg8cDAhoefv4FILQYHgESCQZqM5IwDxGoBEQkMh20MGhKI1cJ+xuxBQsF5HgOGA4wfPuYQoUW+J8fcIMHgNo85cwOz5MxtxFhzg8dMAqTFsuEAGzMvCVrO8RgcSCBNywEStBicSSsDaknmkZxxsJk4v8i3H94m8eGPnT0/f/PBDx+JchgCMDaQpn4UjIJRMApGAW4AALnTMnpyNzcZAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Cunli","middleName":"","lastName":"Song","suffix":""},{"id":463736578,"identity":"d7458640-64ad-4d6a-af4f-cdc134c80a5c","order_by":1,"name":"Yueling Yan","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yueling","middleName":"","lastName":"Yan","suffix":""},{"id":463736579,"identity":"15ae212a-18ca-4b7e-bed1-3c592358e540","order_by":2,"name":"Weiguo Shi","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-05-20 03:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101792646,"identity":"b2d0d109-3224-448d-9fc5-bb06b340fc61","added_by":"auto","created_at":"2026-02-03 16:13:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1011625,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703117/v1_covered_4bdcac74-f80e-4291-b1f0-37a1d4f0c947.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Improved and Efficient PSMD-Yolo Algorithm for Detecting Surface Defects in Fabrics","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":"Fabric Defects, Lightweight Convolution, Feature Fusion, Loss Function","lastPublishedDoi":"10.21203/rs.3.rs-6703117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing the issue of poor accuracy in traditional detection methods for fabrics with significantly varying textures and small defect targets, this paper proposes an improved detection algorithm, PSMD-Yolo, based on YOLOv8. 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