Mask RCNN Chip Surface Defect Detection based on Multi-Scale Grouped and Dual Attention Mechanism

Mask RCNN Chip Surface Defect Detection based on Multi-Scale Grouped and Dual Attention Mechanism | 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 Mask RCNN Chip Surface Defect Detection based on Multi-Scale Grouped and Dual Attention Mechanism Sheng-wei Fei, Rui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6877171/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 low detection precision caused by multiple types of defects on chip surfaces that often overlap, this paper proposes an improved Mask RCNN algorithm for chip surface defect detection based on multi-scale grouped and dual attention mechanisms. The standard convolution in the original ResNet50 network structure is modified to four group convolutions of different scales, with each group using different convolutional kernels. The feature maps obtained from each group are then concatenated to achieve channel stacking. Additionally, the SENet channel attention mechanism and the GCNet spatial attention mechanism are introduced at different stages of the Feature Pyramid Network (FPN) to fully utilize the hierarchical feature representations, thereby enhancing the network's ability to perceive and accurately detect surface defects. In the MPU6050 chip surface defect detection experiment, the average detection precision of the original Mask RCNN algorithm is 93.16%, and the proposed algorithm reaches the average detection precision of 96.35%, which significantly improves the detection performance of MPU6050 chip surface defects. Chip surface defect detection Attention mechanism Mask RCNN Multi-scale grouped convolution 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-6877171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475475642,"identity":"4164b1c4-9442-4db3-b123-04a8572944c1","order_by":0,"name":"Sheng-wei Fei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIie3RoQ7CMBCA4WuWMHNjdih4hCYkqCV7EExr5vATE0eWgMRO8BAodJtLwCzhFUawiD0CRSLIikP00/cnvStAEPyhmASZocogjSLuvRI0YmvbLofZflJKzwQiTnYlyBsuMr8kboix42LJCBLqfD2eoCV7rFifOTE9XMoNjSVFpsk8O1YrniopiMcTnN/JJDv3sAZl5pe4MevWF6fIO0FN7yPrlt2Rlc8uGF8fg/vKIj0w90Odjyef1G/jQRAEwTcviiBFIk2xQuoAAAAASUVORK5CYII=","orcid":"","institution":"Donghua University","correspondingAuthor":true,"prefix":"","firstName":"Sheng-wei","middleName":"","lastName":"Fei","suffix":""},{"id":475475643,"identity":"c6c355b5-5dd1-4ce5-8c40-2da3c20b33c0","order_by":1,"name":"Rui Zhang","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-12 06:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6877171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6877171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99310107,"identity":"367eb67b-8f14-4801-9c89-e5a6a3ddc7e8","added_by":"auto","created_at":"2025-12-31 16:11:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":804077,"visible":true,"origin":"","legend":"","description":"","filename":"MACFMaskRCNNchipsurfacedefectdetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6877171/v1_covered_51141c71-0986-4475-a089-2f7c2f8aac45.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mask RCNN Chip Surface Defect Detection based on Multi-Scale Grouped and Dual Attention Mechanism","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":"Chip surface defect detection, Attention mechanism ,Mask RCNN, Multi-scale grouped convolution","lastPublishedDoi":"10.21203/rs.3.rs-6877171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6877171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing the issue of low detection precision caused by multiple types of defects on chip surfaces that often overlap, this paper proposes an improved Mask RCNN algorithm for chip surface defect detection based on multi-scale grouped and dual attention mechanisms. 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