Lightweight Improved YOLOv5 Algorithm for PCB Defect Detection

Lightweight Improved YOLOv5 Algorithm for PCB 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 Lightweight Improved YOLOv5 Algorithm for PCB Defect Detection Yinggang Xie, Yanwei Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734440/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract A lightweight YOLOv5 improved algorithm-based inspection model is proposed to address the problems of defective printed circuit boards (PCBs), which are difficult to identify. First, the detection part of YOLOv5 is changed to dual-head detection to significantly improve the inference speed of the model on edge devices and adapt to the real-time target detection requirements. Second, the introduction of GSConv in the Neck part helps to further reduce the number of parameters of the model and improve the computational efficiency, which can enhance the model's capture ability.Finally, BiFPN is introduced to fuse multi-scale information to enhance the model's detection ability for targets of different sizes. The experimental results show that the improved lightweight YOLOv5 algorithm in this paper achieves 94.9% in the average accuracy mean ( [email protected] ), which is only 0.5 percentage points less compared to the original YOLOv5s algorithm. However, the improved algorithm has 56.2% fewer floating point operations (GFLOPs) and 53.7% fewer parameters. This improvement not only makes the algorithm more accurate and lightweight, but also significantly improves the efficiency of PCB inspection, which better meets the needs of industrial production. yolov5s algorithm GSConv detection layer improvement BiFPN attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 22 Jul, 2024 Submission checks completed at journal 22 Jul, 2024 First submitted to journal 13 Jul, 2024 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-4734440","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333931390,"identity":"7171f289-ba34-4107-a253-e1f0e5e5743e","order_by":0,"name":"Yinggang Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACZiBOgDAZHzMYQFgSxGphNgZqkSCsBQmwScNU49VicJz5mMTDHbV2/bPbr1UXFNTVGRxgPnibh8EuD5cWyWa2NInEM8eTZ9w5U3Z7hsFhCYMDbMnWPAzJxbi08DPzmEkkth1LZriRk3abx+AAUAuPmTQPw4HEBlzuZ+b/BtYiD9RSzGNQB9TC/w2vFqAtbEAtNXYGN9KPMfMYMINsYcOrBegXY4vEtgMJhjdymKWBfpGceZjN2HKOQTJOLQbnDz+8+bOtzl7uRvrDzwV/6vj5jjc/vPGmwg6nFiBgAcbCYaACHmjUM4ONwq0epOQDA0OdPQMD+wO8ykbBKBgFo2DkAgDOTFP3OVMm5gAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yinggang","middleName":"","lastName":"Xie","suffix":""},{"id":333931391,"identity":"199c2a23-3b53-4f5d-b216-584dec56bde4","order_by":1,"name":"Yanwei Zhao","email":"","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanwei","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-07-13 09:07:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4734440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4734440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62758693,"identity":"28ae866c-1568-4a30-ba9c-eba17a532508","added_by":"auto","created_at":"2024-08-19 07:10:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":737829,"visible":true,"origin":"","legend":"","description":"","filename":"LightweightImprovedYOLOv5AlgorithmforPCBDefectDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4734440/v1_covered_8e5ffb10-3e38-4489-860a-c78dac895599.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lightweight Improved YOLOv5 Algorithm for PCB Defect Detection","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"yolov5s algorithm, GSConv, detection layer improvement, BiFPN, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-4734440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4734440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA lightweight YOLOv5 improved algorithm-based inspection model is proposed to address the problems of defective printed circuit boards (PCBs), which are difficult to identify. First, the detection part of YOLOv5 is changed to dual-head detection to significantly improve the inference speed of the model on edge devices and adapt to the real-time target detection requirements. Second, the introduction of GSConv in the Neck part helps to further reduce the number of parameters of the model and improve the computational efficiency, which can enhance the model's capture ability.Finally, BiFPN is introduced to fuse multi-scale information to enhance the model's detection ability for targets of different sizes. The experimental results show that the improved lightweight YOLOv5 algorithm in this paper achieves 94.9% in the average accuracy mean ([email protected]), which is only 0.5 percentage points less compared to the original YOLOv5s algorithm. However, the improved algorithm has 56.2% fewer floating point operations (GFLOPs) and 53.7% fewer parameters. This improvement not only makes the algorithm more accurate and lightweight, but also significantly improves the efficiency of PCB inspection, which better meets the needs of industrial production.\u003c/p\u003e","manuscriptTitle":"Lightweight Improved YOLOv5 Algorithm for PCB Defect Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-19 07:02:25","doi":"10.21203/rs.3.rs-4734440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-22T21:08:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-22T21:07:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2024-07-13T09:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a63b2bee-3ed0-4a0f-b866-3a3d4e5c848f","owner":[],"postedDate":"August 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-16T15:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-19 07:02:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4734440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4734440","identity":"rs-4734440","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}