Ultra-Lightweight YOLOv8n for PCB Defect Detection: An Adaptive Approach with Enhanced Feature Extraction and Efficient Model Embedding

Ultra-Lightweight YOLOv8n for PCB Defect Detection: An Adaptive Approach with Enhanced Feature Extraction and Efficient Model Embedding | 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 Ultra-Lightweight YOLOv8n for PCB Defect Detection: An Adaptive Approach with Enhanced Feature Extraction and Efficient Model Embedding Zhuguo Zhou, Yujun Lu, Liye Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4737577/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 To address the issues of missed and false detections caused by numerous tiny objects and complex background textures in printed circuit boards (PCBs), as well as the difficulty of embedding detection models into portable devices, this paper proposes an ultra-lightweight YOLOv8n defect detection method. Firstly, the method introduces an Uncertainty-driven Adaptive Training Sample Selection (UATSS) strategy during training to optimize model training and enhance detection accuracy. Secondly, it incorporates Details-Enhanced Convolution (DEConv) to improve the model's ability to extract detailed features of small PCB defects. Then, it employs a Sharing Lightweight Details-Enhanced Convolutional Detection Head (SLDECD) to replace the original Decoupled Head, reducing model complexity while enhancing network detection accuracy. Lastly, the Exponential Moving Average-Slide Loss (EMA-SlideLoss) function is introduced to provide more precise evaluation results during model training and enhance generalization capability. Comparative experiments on public PCB datasets demonstrate that the improved algorithm achieves an mAP of 97.6% and an accuracy of 99.6%, representing increases of 3.8% and 1.9%, respectively, compared to the original model. The model size is 4.1 MB, and the FPS reaches 144.1, meeting the requirements for portable embedded devices and real-time applications. Printed circuit board YOLOv8n Detail-Enhanced Convolution Lightweight detection head 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. 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-4737577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330570005,"identity":"9a129b67-c7ba-4de5-bff6-0f1bb1d489fd","order_by":0,"name":"Zhuguo Zhou","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Zhuguo","middleName":"","lastName":"Zhou","suffix":""},{"id":330570006,"identity":"73a71474-0df1-4316-888e-4f9c069c33f9","order_by":1,"name":"Yujun Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACPjBZwMDMDxVgbCCkhQ2syICBWbIBopp4LQwGB4jWIpF8/AGDwWF24/PHnz/mYbCR3XCA+dkD/FrSEoG2HGY2O3DGsJmHIc14wwE2cwP8WnIMIVoO9jACtRxO3HCAh00Cv5b8j2Atxs3sD4Fa/hOjJYcRrMWAjQHksANEaOF5ZjiDwSCdWeIMj+HMOQbJxjMPs5nh1cLPnvzgA0OFdTJ///EHH95U2Mn2HW9+hlcLCDD/YWhOhjBBQcVMSD0E1NkRp24UjIJRMApGJAAAUEVBqfsvyaEAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":true,"prefix":"","firstName":"Yujun","middleName":"","lastName":"Lu","suffix":""},{"id":330570010,"identity":"bbc80971-5b1c-4e47-848a-2894c02cb745","order_by":2,"name":"Liye Lv","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Liye","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2024-07-14 08:16:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4737577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4737577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63195407,"identity":"accf8831-7a50-43f4-962d-ad5c01d41120","added_by":"auto","created_at":"2024-08-24 18:21:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1661357,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4737577/v1_covered_6127aa05-a6af-4725-917e-bd0d251ececc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ultra-Lightweight YOLOv8n for PCB Defect Detection: An Adaptive Approach with Enhanced Feature Extraction and Efficient Model Embedding","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":"Printed circuit board, YOLOv8n, Detail-Enhanced Convolution, Lightweight detection head, Loss function","lastPublishedDoi":"10.21203/rs.3.rs-4737577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4737577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the issues of missed and false detections caused by numerous tiny objects and complex background textures in printed circuit boards (PCBs), as well as the difficulty of embedding detection models into portable devices, this paper proposes an ultra-lightweight YOLOv8n defect detection method. Firstly, the method introduces an Uncertainty-driven Adaptive Training Sample Selection (UATSS) strategy during training to optimize model training and enhance detection accuracy. Secondly, it incorporates Details-Enhanced Convolution (DEConv) to improve the model's ability to extract detailed features of small PCB defects. Then, it employs a Sharing Lightweight Details-Enhanced Convolutional Detection Head (SLDECD) to replace the original Decoupled Head, reducing model complexity while enhancing network detection accuracy. Lastly, the Exponential Moving Average-Slide Loss (EMA-SlideLoss) function is introduced to provide more precise evaluation results during model training and enhance generalization capability. Comparative experiments on public PCB datasets demonstrate that the improved algorithm achieves an mAP of 97.6% and an accuracy of 99.6%, representing increases of 3.8% and 1.9%, respectively, compared to the original model. 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