Deep Learning Based Defect Detection Method for Overhead Transmission Wires

Deep Learning Based Defect Detection Method for Overhead Transmission Wires | 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 Article Deep Learning Based Defect Detection Method for Overhead Transmission Wires Zhilong Yu, Yanqiao Lei, Feng Shen, Shuai Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4293661/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 Transmission line is the carrier of power transmission, in order to more accurately detect the transmission conductor is susceptible to the influence of the external environment and cause the conductor to break the strand, loose strand to the cable foreign body hanging and other defects. In this paper, a lightweight transmission conductor defect detection algorithm named HorCM_PAM_YOLOv5 based on YOLOv5 is proposed. Firstly, in order to increase the algorithm's ability to spatially interact with different features as well as its detection accuracy, this paper designs a lightweight recursive convolution module HorCM with reference to HorNet to enhance the expression of the algorithm's model. Then in order to cope with the problem of complex background and difficult detection of aerial images, this paper proposes a lightweight parallel attention mechanism module (PAM), so that the defective image channel feature extraction and spatial feature extraction can be independent of each other, which reduces the interference of the background and increases the image's characterisation ability. Afterwards, in order to cope with the problem of large differences in the scale of wire defect images taken by aerial photography, this paper proposes for the MPDIOU loss function, using the distance between the prediction frame and the actual frame of the identified points to minimise the processing, to improve the accuracy of the model and convergence speed. Finally, in order to better reflect the actual engineering application scenarios, our dataset uses UAS machine patrol images from Yunnan Power Supply Bureau Company. Experimental simulations show that with a 3.1% increase in detection speed relative to YOLOv5s and a 5% reduction in model volume, the improved algorithm HorCM_PAM_YOLOv5 still manages to increase its overall performance by 9.8% over YOLOv5s, and its accuracy by 7.2% over YOLOv5s. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Electrical and electronic engineering Transmission line Defect detection HorNet Attention mechanism HorCM_PAM_YOLOv5 MPDIOU Loss 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-4293661","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299879130,"identity":"d7abf6b3-156d-4314-8c0a-70e9e3306a51","order_by":0,"name":"Zhilong Yu","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhilong","middleName":"","lastName":"Yu","suffix":""},{"id":299879132,"identity":"56c9d01b-a959-4f56-81c4-85c7c0e2bc6f","order_by":1,"name":"Yanqiao Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3PsWrDMBCAYQmDtKh2RxlB8wqnBkKhIu8iDJo8dMxWB4GmQFc/hh/BztF0yQMYksGh0DljhwztAwQ73Tron264j+MIicX+YSmh6+EM8iGbYfc7mOUkYYR6Xa/MPK9ZoesXV9xACFNi72zTi4US5y2tJgn3Hu4C0uYQnDLQJoTjezNKRLce8oBJfkR8LuGYEuFcP0qk9aADsrR19lDCV0KkWIyT2SlIG1CQtgT1BEirSSJpkO3eyfu+fFTkJiKs19XKQL7ZFXoDrmBTv2T84/R5Afn6xn03fF/MMuO4GyVX7v5tPRaLxWLX+gFNuE62MQvTVAAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanqiao","middleName":"","lastName":"Lei","suffix":""},{"id":299879134,"identity":"775ee9dc-9956-49f9-8291-0f5cccea2e3e","order_by":2,"name":"Feng Shen","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Shen","suffix":""},{"id":299879136,"identity":"508f00d0-921b-4671-949c-d6066f177e32","order_by":3,"name":"Shuai Zhou","email":"","orcid":"","institution":"Yunnan Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-04-19 14:14:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4293661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4293661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92703256,"identity":"441992fb-b924-4caa-9079-82770461eb57","added_by":"auto","created_at":"2025-10-03 09:02:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":870900,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchondefectdetectionmethodofoverheadtransmissionconductorbasedonYOLOv5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4293661/v1_covered_17bf755d-eb29-46e8-9893-b0b7baad2b5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning Based Defect Detection Method for Overhead Transmission Wires","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":"Transmission line, Defect detection, HorNet, Attention mechanism, HorCM_PAM_YOLOv5, MPDIOU Loss","lastPublishedDoi":"10.21203/rs.3.rs-4293661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4293661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransmission line is the carrier of power transmission, in order to more accurately detect the transmission conductor is susceptible to the influence of the external environment and cause the conductor to break the strand, loose strand to the cable foreign body hanging and other defects. In this paper, a lightweight transmission conductor defect detection algorithm named HorCM_PAM_YOLOv5 based on YOLOv5 is proposed. Firstly, in order to increase the algorithm's ability to spatially interact with different features as well as its detection accuracy, this paper designs a lightweight recursive convolution module HorCM with reference to HorNet to enhance the expression of the algorithm's model. Then in order to cope with the problem of complex background and difficult detection of aerial images, this paper proposes a lightweight parallel attention mechanism module (PAM), so that the defective image channel feature extraction and spatial feature extraction can be independent of each other, which reduces the interference of the background and increases the image's characterisation ability. Afterwards, in order to cope with the problem of large differences in the scale of wire defect images taken by aerial photography, this paper proposes for the MPDIOU loss function, using the distance between the prediction frame and the actual frame of the identified points to minimise the processing, to improve the accuracy of the model and convergence speed. Finally, in order to better reflect the actual engineering application scenarios, our dataset uses UAS machine patrol images from Yunnan Power Supply Bureau Company. 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