Surface Defect Detection of Magnetic Tiles Based on an Improved Lightweight GhostNet Network

Surface Defect Detection of Magnetic Tiles Based on an Improved Lightweight GhostNet Network | 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 Surface Defect Detection of Magnetic Tiles Based on an Improved Lightweight GhostNet Network Xingwang Shang, Xueqin Li, Peng Shi, Weibing Zhu, Jiayu Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6020940/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 In modern industry, the performance of permanent magnet motors is crucial to enhancing efficiency and system reliability. As a key component of these motors, the quality of magnetic tiles directly impacts motor performance. Therefore, ensuring that magnetic tile surfaces are free of defects is essential for maintaining product quality. While traditional visual inspection methods have been widely used, they suffer from limitations in precision and stability. Although deep learning has improved detection accuracy, it increases the demand on computational resources. To address these issues, this paper proposes an optimized lightweight deep learning model called S-GhostNet, which aims to enhance the efficiency and accuracy of magnetic tile surface defect detection while reducing computational complexity. S-GhostNet employs advanced optimization techniques, such as generating Ghost features using different dilation rates in convolutional layers to capture multi-scale defect information, thereby enhancing feature diversity. Channel shuffling and depthwise separable convolutions promote feature fusion and reduce redundant computations. Additionally, the integration of Feature Pyramid Networks (FPN) improves the detection of defects of various sizes. Experimental results show that S-GhostNet achieves an accuracy of 95.46% in magnetic tile surface defect detection, achieving a 14.36% improvement over the original GhostNet, while reducing computational cost (FLOPs) by approximately 29.76%, from 4.20 G to 2.95 G. This demonstrates that S-GhostNet not only enhances detection accuracy but also significantly reduces the required computational resources, highlighting its advantages in this field. Surface defect detection of magnetic tiles deep learning GhostNet lightweight convolutional neural network 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-6020940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415265178,"identity":"bb626b08-9980-4313-99ad-deb8f85dbd12","order_by":0,"name":"Xingwang Shang","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Xingwang","middleName":"","lastName":"Shang","suffix":""},{"id":415265179,"identity":"7b0ba88a-3b2f-4095-9c55-d6c2896e0100","order_by":1,"name":"Xueqin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACfv7mgw8+/PgnR7wWyRnHkg1n9hwwJl6LQUOOmTAP24HEBuK1MBwwY+bhuZPedzyB8cPHHCK0mDM3pD2cY/Esd+aZB8ySM7cRocWy4cBxgzc8zLkbbiSwMfMSo8XgQGKbBA8bc7oBCVqS2SR52A4nEK8FGMjMwEBOM5x55mEzcX7h5+//CIxKG3m+48kHP3wkRgsCkBI1MC0JpOoYBaNgFIyCkQIAbqo+kfp2V6gAAAAASUVORK5CYII=","orcid":"","institution":"Xihua University","correspondingAuthor":true,"prefix":"","firstName":"Xueqin","middleName":"","lastName":"Li","suffix":""},{"id":415265180,"identity":"a2a2e205-1e69-485e-84d2-ac419f6429e1","order_by":2,"name":"Peng Shi","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Shi","suffix":""},{"id":415265181,"identity":"f2ef5f90-0ce4-4eae-b3e8-3ce484eea25b","order_by":3,"name":"Weibing Zhu","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Weibing","middleName":"","lastName":"Zhu","suffix":""},{"id":415265182,"identity":"fe34ec0e-088c-44e5-b100-ca9d831b5f56","order_by":4,"name":"Jiayu Jiang","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-02-13 08:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6020940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6020940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78190200,"identity":"6848a646-8ebf-427e-9658-f02513994899","added_by":"auto","created_at":"2025-03-10 19:47:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1510312,"visible":true,"origin":"","legend":"","description":"","filename":"LaTeXDL468198240419.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6020940/v1_covered_65e87fe5-af2d-4884-bb19-05c66efcf07e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surface Defect Detection of Magnetic Tiles Based on an Improved Lightweight GhostNet Network","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":"Surface defect detection of magnetic tiles, deep learning, GhostNet, lightweight convolutional neural network","lastPublishedDoi":"10.21203/rs.3.rs-6020940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6020940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In modern industry, the performance of permanent magnet motors is crucial to enhancing efficiency and system reliability. As a key component of these motors, the quality of magnetic tiles directly impacts motor performance. Therefore, ensuring that magnetic tile surfaces are free of defects is essential for maintaining product quality. While traditional visual inspection methods have been widely used, they suffer from limitations in precision and stability. Although deep learning has improved detection accuracy, it increases the demand on computational resources. To address these issues, this paper proposes an optimized lightweight deep learning model called S-GhostNet, which aims to enhance the efficiency and accuracy of magnetic tile surface defect detection while reducing computational complexity. S-GhostNet employs advanced optimization techniques, such as generating Ghost features using different dilation rates in convolutional layers to capture multi-scale defect information, thereby enhancing feature diversity. Channel shuffling and depthwise separable convolutions promote feature fusion and reduce redundant computations. Additionally, the integration of Feature Pyramid Networks (FPN) improves the detection of defects of various sizes. Experimental results show that S-GhostNet achieves an accuracy of 95.46\\% in magnetic tile surface defect detection, achieving a 14.36\\% improvement over the original GhostNet, while reducing computational cost (FLOPs) by approximately 29.76\\%, from 4.20 G to 2.95 G. 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