AE-YOLOv5 for Detection of Power Line Insulator Defects

AE-YOLOv5 for Detection of Power Line Insulator Defects | 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 AE-YOLOv5 for Detection of Power Line Insulator Defects Wei Shen, Ming Fang, Yuxia Wang, Weifeng Zhang, Jiafeng Xiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996139/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 The power transmission network, which delivers power energy from generator to customers, plays an important role in the power grid. Insulator is a basic component in the power transmission network. Its defects may lead to the paralysis of the entire transmission network, resulting in serious electricity accidents. Therefore, how to use artificial intelligence and other emerging technologies to realize automatic detection of power line insulator defects has become an urgent problem to be solved. To accurately detect insulator defects in complex environment, this paper proposes Attention Enhanced YOLOv5 (AE-YOLOv5) by inserting visual attention modules into original YOLOv5 model. In particular, we design a Channel-Spatial Attention module and plug it into the backbone of YOLOv5 to enhance its representation learning ability. Furthermore, a Multi-scale Attention module is also proposed to enhance the Feature Pyramid Network (FPN). To validate the efficacy of our proposed model, we conducted training and testing on a dataset collected from real-world scenarios. The experimental results demonstrate that our model can effectively and accurately detect defects of power line insulators in real-time. Power line insulator Defect detection Deep learning 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-3996139","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275366757,"identity":"daf8965f-e3f6-49fd-bfca-5bb1e7b33918","order_by":0,"name":"Wei Shen","email":"","orcid":"","institution":"Metrological Verification and Testing Institute of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shen","suffix":""},{"id":275366758,"identity":"306f8892-ce02-4f9d-90b2-c1a1e38d6ed5","order_by":1,"name":"Ming Fang","email":"","orcid":"","institution":"Metrological Verification and Testing Institute of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Fang","suffix":""},{"id":275366759,"identity":"9101ca83-f703-47bf-a626-7e4c68710d2f","order_by":2,"name":"Yuxia Wang","email":"","orcid":"","institution":"Metrological Verification and Testing Institute of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Yuxia","middleName":"","lastName":"Wang","suffix":""},{"id":275366760,"identity":"e1602008-c63a-4b3e-b299-ed82d8cd1977","order_by":3,"name":"Weifeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDACCQjFw94AJD8wsCELEtDCc4CBgXEGKVoYQFqYedAEsQL52c3PHn5ts5HhYe89/NrmD1/ihgPMB2/zMNjl4dLCOOeYubFsWxoPD8+5NOscHjZjgwNsydY8DMnFuLQwSySYSUtuO8xjL5FjZpwjwSZncIDHTJqH4UBiAw4tbBLp34Ba/vPwyL8xM7YwYOMxOMD/Da8WHqDhkh+3HeDhkeAxfsyQALaFDa8WCYmcMmnGf8lAv+SYMfYcYDOWPMxmbDnHIBmnFvkZ6dskf5yxs+dhP2P84cefY4l9x5sf3nhTYYdTCzgIeGD+YmA4BuSC2AZ41AMB4w+o1g8MDDX4lY6CUTAKRsGIBADvnEv6V5lwUwAAAABJRU5ErkJggg==","orcid":"","institution":"Jiaxing University","correspondingAuthor":true,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Zhang","suffix":""},{"id":275366761,"identity":"0dc10d66-0087-4f4d-9cce-44e25bbf3af6","order_by":4,"name":"Jiafeng Xiao","email":"","orcid":"","institution":"Metrological Verification and Testing Institute of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Jiafeng","middleName":"","lastName":"Xiao","suffix":""},{"id":275366762,"identity":"6e3c7b04-30a6-4990-bd14-bc14c1247ddb","order_by":5,"name":"Huangqun Chen","email":"","orcid":"","institution":"Metrological Verification and Testing Institute of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Huangqun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-02-28 08:45:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3996139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53496906,"identity":"89105e82-168e-4f0d-8b62-644d713673df","added_by":"auto","created_at":"2024-03-26 17:07:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811929,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996139/v1_covered_df2ac4bf-1e4b-47b1-bd02-c054112dd7a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AE-YOLOv5 for Detection of Power Line Insulator Defects","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":"Power line insulator; Defect detection; Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-3996139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The power transmission network, which delivers power energy from generator to customers, plays an important role in the power grid. 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