PCB-YOLOV8X: A network for detecting micro-sized defects on PCB surfaces based on enhanced feature information

PCB-YOLOV8X: A network for detecting micro-sized defects on PCB surfaces based on enhanced feature information | 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 PCB-YOLOV8X: A network for detecting micro-sized defects on PCB surfaces based on enhanced feature information Xiaoyan Xu, Jennifer C. Dela Cruz, Ye Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8631663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract To address the challenges in PCB surface defect detection, including high labor costs, low detection efficiency, and insufficient detection accuracy, this study innovatively proposes a PCB surface micro-defect detection network called PCB-YOLOV8X based on the YOLOv8 model. By integrating the C2f-DCNV2 adaptive feature extraction module and the SPPF-LSKA efficient feature enhancement module, the network significantly improves the detection accuracy and efficiency for micro-sized defects. Furthermore, an optimized IWD-CIoU loss function is proposed to more precisely evaluate the matching degree between predicted and ground truth bounding boxes. In comparative experiments with different models, PCB-YOLOV8X achieves optimal performance on key metrics, including [email protected] of 98.44%, Precision of 97.67%, and Recall of 96.34%. Compared with the traditional YOLOv8 model, these represent improvements of 2.76% in [email protected] , 2.33% in Precision, and 2.59% in Recall. The proposed PCB-YOLOv8X demonstrates enhanced capability in feature extraction and fusion in PCB surface micro-defect detection tasks, effectively solving issues such as low accuracy, missed detections, and false alarms in existing PCB defect detection models. Physical sciences/Engineering Physical sciences/Mathematics and computing PCB surface defect detection Yolov8 model micro-sized defects adaptive feature extraction feature enhancement loss function Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 05 Feb, 2026 Editor assigned by journal 01 Feb, 2026 Submission checks completed at journal 01 Feb, 2026 First submitted to journal 18 Jan, 2026 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-8631663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588959876,"identity":"2a720147-ca71-429b-bea7-dbcefd00b4a1","order_by":0,"name":"Xiaoyan Xu","email":"","orcid":"","institution":"Lishui University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Xu","suffix":""},{"id":588959877,"identity":"fd551f8f-0fa0-4104-9b4e-332158271f39","order_by":1,"name":"Jennifer C. Dela Cruz","email":"","orcid":"","institution":"Mapúa University","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"C. Dela","lastName":"Cruz","suffix":""},{"id":588959878,"identity":"e1638ae4-23e5-4f4a-90fa-d5e45351e1ae","order_by":2,"name":"Ye Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNoYEIMnPzNj84EMBWMSAOC2S7c1thjMMiNUCVnbmeIM0DzFaDG4kP3vwcIcdA8ONxAZjG4O6xAb25m0SDDV38GhJMzdIPJPMwDgjseFxjsHhxAaeY2USDMee4dRidiOHTSKxjZmBWQJoS47BgcQGiRwzCcaGw4S01DMAyQZpC5DD5N8QpeUwAw/PwQZpBgNmoC08+LXYn3lmBtRynEGCvbHNsMfgsHEbT1qxRcIx3Fok25OfSf5sq2awP8z++MGPijrZfvbDG298qMGthUEgAUzVN8AEwNGUgFsDMKEcwCc7CkbBKBgFowAIAA0UUvyjKFuoAAAAAElFTkSuQmCC","orcid":"","institution":"Lishui University","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-18 13:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8631663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8631663/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-46089-6","type":"published","date":"2026-03-27T16:10:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105755539,"identity":"51e084de-3615-407f-b29e-27a655efbcad","added_by":"auto","created_at":"2026-03-30 16:27:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1159020,"visible":true,"origin":"","legend":"","description":"","filename":"PCBYOLOV8XAnetworkfordetectingmicrosizeddefectsonPCBsurfacesbasedonenhancedfeatureinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8631663/v1_covered_5998bec8-d339-4674-92ca-687f70b865bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PCB-YOLOV8X: A network for detecting micro-sized defects on PCB surfaces based on enhanced feature information","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PCB surface defect detection, Yolov8 model, micro-sized defects, adaptive feature extraction, feature enhancement, loss function","lastPublishedDoi":"10.21203/rs.3.rs-8631663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8631663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the challenges in PCB surface defect detection, including high labor costs, low detection efficiency, and insufficient detection accuracy, this study innovatively proposes a PCB surface micro-defect detection network called PCB-YOLOV8X based on the YOLOv8 model. By integrating the C2f-DCNV2 adaptive feature extraction module and the SPPF-LSKA efficient feature enhancement module, the network significantly improves the detection accuracy and efficiency for micro-sized defects. Furthermore, an optimized IWD-CIoU loss function is proposed to more precisely evaluate the matching degree between predicted and ground truth bounding boxes. In comparative experiments with different models, PCB-YOLOV8X achieves optimal performance on key metrics, including [email protected] of 98.44%, Precision of 97.67%, and Recall of 96.34%. Compared with the traditional YOLOv8 model, these represent improvements of 2.76% in [email protected], 2.33% in Precision, and 2.59% in Recall. The proposed PCB-YOLOv8X demonstrates enhanced capability in feature extraction and fusion in PCB surface micro-defect detection tasks, effectively solving issues such as low accuracy, missed detections, and false alarms in existing PCB defect detection models.\u003c/p\u003e","manuscriptTitle":"PCB-YOLOV8X: A network for detecting micro-sized defects on PCB surfaces based on enhanced feature information","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 03:45:06","doi":"10.21203/rs.3.rs-8631663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-13T10:20:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T15:03:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T12:07:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73686394458769416587100594402066655002","date":"2026-02-08T13:29:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285632412336627592182427266353596126971","date":"2026-02-08T12:32:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T12:16:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T10:32:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T04:22:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T04:20:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-18T13:46:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef77c734-12c6-4871-b30d-023bfe0ea4f2","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":62662733,"name":"Physical sciences/Engineering"},{"id":62662734,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-30T16:25:48+00:00","versionOfRecord":{"articleIdentity":"rs-8631663","link":"https://doi.org/10.1038/s41598-026-46089-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-27 16:10:33","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2026-02-11 03:45:06","video":"","vorDoi":"10.1038/s41598-026-46089-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-46089-6","workflowStages":[]},"version":"v1","identity":"rs-8631663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8631663","identity":"rs-8631663","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}