Lightweight YOLOv7 for bushing surface defects detection

Lightweight YOLOv7 for bushing surface defects 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 YOLOv7 for bushing surface defects detection Wenjun Cheng, Pengfei Zeng, Yongping Hao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4708700/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 16 You are reading this latest preprint version Abstract Bushings as metal structure parts because of their good welding performance, good plasticity strength, and other advantages in the engineering field are widely used. However, because the performance of the internal microstructure of the metal material is not uniform, there is an electrode potential difference between the micro area leading to surface corrosion; and the production process will inevitably produce defective surfaces with defective products, thus seriously affecting the subsequent use. Therefore, it is essential to accurately detect the defects on the surface of the bushing.At present, the inspection method based on machine vision has replaced the manual inspection method with low efficiency and a high false detection rate. However,due to the large amount of computation brought about by the complex network model, the efficiency can not meet the production needs of real-time detection; the simple network model is due to the limited ability to extract features and thus can not meet the requirements of the accuracy of the detection. To ensure the detection accuracy of the surface defects of the bushing and at the same time reduce the volume of the model, a bushing defect detection model based on the improved YOLOv7 is proposed. The backbone network of the model uses MobileNetv3 to replace the backbone network of the original YOLOv7, which improves the detection speed while guaranteeing the detection accuracy, and realizes the lightweight model at the same time; the CBAM (Convolutional Block Attention Module) attention mechanism is introduced into the residual edges of each layer of the backbone network, which pays more attention to the small-size target to get more important feature information; BiFPN feature pyramid is used to optimize the detection effect by weighted fusion of multi-scale feature information. The experimental results show that the improved algorithm in this paper reduces the mAP by only 0.7 percent compared with the traditional YOLOv7 algorithm; however, the detection speed is increased by 29.4 percent, and the model volume is reduced by 29.9 percent, which effectively improves the detection accuracy and speed of all kinds of defects on the surface of the bushings, and it can be better adapted to the industrial detecting environment. Mathematics Subject Classification (2020) 5206050 CBAM MobileNetv3 YOLOv7 Surface defect detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 19 Jul, 2024 Reviews received at journal 19 Jul, 2024 Reviews received at journal 18 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers agreed at journal 11 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 10 Jul, 2024 Editor assigned by journal 10 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 08 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. 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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-4708700","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329260106,"identity":"a565929d-64bd-4e58-94b8-9c5d28717933","order_by":0,"name":"Wenjun Cheng","email":"","orcid":"","institution":"Shenyang Ligong University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Cheng","suffix":""},{"id":329260107,"identity":"8c6cb2aa-2309-4f2b-8bb8-892ee3c578cc","order_by":1,"name":"Pengfei Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDAC5gMg0oaHn7+BWC1sCSAyTUZyxgHStBy2MWhIIFKHORv7w8cFv87zGDAcYPzwMYcILZZtDMnGM/tu85gzNzBLztxGhBaD+w3HpHl7bvNYNhxgY+YlSssxxvbfvD3neAwOJBCthZmNmefHARK0WLaxMUvzNiTzSM442EycX0Ah9pnnj509P3/zwQ8fiXIYiGBsA5MNRKiHaWH4Q5ziUTAKRsEoGKEAAD6mM+xm7kS2AAAAAElFTkSuQmCC","orcid":"","institution":"Shenyang Ligong University","correspondingAuthor":true,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Zeng","suffix":""},{"id":329260108,"identity":"79186615-6a74-4385-a8b4-3f23bde492f8","order_by":2,"name":"Yongping Hao","email":"","orcid":"","institution":"Shenyang Ligong University","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2024-07-09 02:57:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4708700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4708700/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11554-025-01630-0","type":"published","date":"2025-02-07T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930457,"identity":"7769cd41-e01e-4c24-84ea-8b7ed2db9d12","added_by":"auto","created_at":"2025-02-10 16:12:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1690318,"visible":true,"origin":"","legend":"","description":"","filename":"LightweightYOLOv7forbushingsurfacedefectsdetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4708700/v1_covered_4a189450-084d-4146-a9e8-f6ec343835de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lightweight YOLOv7 for bushing surface defects 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":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CBAM, MobileNetv3, YOLOv7, Surface defect detection","lastPublishedDoi":"10.21203/rs.3.rs-4708700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4708700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBushings as metal structure parts because of their good welding performance, good plasticity strength, and other advantages in the engineering field are widely used. However, because the performance of the internal microstructure of the metal material is not uniform, there is an electrode potential difference between the micro area leading to surface corrosion; and the production process will inevitably produce defective surfaces with defective products, thus seriously affecting the subsequent use. Therefore, it is essential to accurately detect the defects on the surface of the bushing.At present, the inspection method based on machine vision has replaced the manual inspection method with low efficiency and a high false detection rate. However,due to the large amount of computation brought about by the complex network model, the efficiency can not meet the production needs of real-time detection; the simple network model is due to the limited ability to extract features and thus can not meet the requirements of the accuracy of the detection. To ensure the detection accuracy of the surface defects of the bushing and at the same time reduce the volume of the model, a bushing defect detection model based on the improved YOLOv7 is proposed. The backbone network of the model uses MobileNetv3 to replace the backbone network of the original YOLOv7, which improves the detection speed while guaranteeing the detection accuracy, and realizes the lightweight model at the same time; the CBAM (Convolutional Block Attention Module) attention mechanism is introduced into the residual edges of each layer of the backbone network, which pays more attention to the small-size target to get more important feature information; BiFPN feature pyramid is used to optimize the detection effect by weighted fusion of multi-scale feature information. 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