Transmission Line Defect Detection via an Integrated Improved YOLOv8 and Deep Neural Random Forest Framework

Transmission Line Defect Detection via an Integrated Improved YOLOv8 and Deep Neural Random Forest Framework | 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 Transmission Line Defect Detection via an Integrated Improved YOLOv8 and Deep Neural Random Forest Framework Liangshuai Liu, Lingming Meng, Anchang Li, Peng Yan, Yuntao Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8831053/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract How to effectively identify defects in power transmission lines using unmanned aerial vehicle (UAV) technology remains a key research focus. This paper presents an optimization over traditional convolutional neural networks by proposing a spatially deformable convolution (SDC) algorithm to enhance feature extraction from images. Additionally, a more comprehensive hybrid loss function is introduced to improve the model's ability to recognize multi-scale defect patterns. A deep neural decision forest (DNDF) is then employed to perform fine-grained classification of candidate regions, outputting precise defect categories. Experimental validation shows that the proposed algorithm achieves recognition accuracy above 92% for five types of conditions: normal, stains, cracks, corrosion, and surface peeling. Compared with several conventional detection methods, the algorithm demonstrates notable improvements. For instance, relative to Faster R-CNN, it increases detection speed by 36 FPS and reduces the number of parameters by 54.57%. When compared with YOLOv7-M, YOLOv9-C, and YOLOv11-M, although inference speed is slightly reduced, the mean average precision (mAP) is improved by 2.6%, 2.38%, and 1.9%, respectively, due to increased algorithmic complexity. These results confirm that the proposed approach can effectively identify multi-scale defects in transmission lines. Physical sciences/Engineering Physical sciences/Mathematics and computing Aerial vehicle SDC Deep neural decision forest Hybrid loss function Multi-scale recognition Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers invited by journal 18 Feb, 2026 Editor invited by journal 17 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 09 Feb, 2026 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. 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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-8831053","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593386674,"identity":"c68cebd1-60a1-465d-ab2b-61cadcba436d","order_by":0,"name":"Liangshuai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACPnYGNiAlIcfG3nzwQUKFDWEtbMwQLcb8PMeSDR6cSSNaC0PizBk5apIP2w4Ro4X92WOeGgvGDQdy2CoS2A4w8Ld3JxDQwmNuzHNMgtngwNljNxJ47jBInDm7gZAWNmkeNgk2g4N9aTcSJJ4xGEjkEtLC/kya558Ej8FhHrOCBIPDxGhhMJPmbZOQkGzjMWNISCBKC4+Z5Nw+CQN+HrZkiYQDaTwE/cLP3v5M4s23uvo2+ccHP/78ZyPH396LXwsG4CFN+SgYBaNgFIwCrAAA00A/qc7g09YAAAAASUVORK5CYII=","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Liangshuai","middleName":"","lastName":"Liu","suffix":""},{"id":593386675,"identity":"897662b2-0236-43a9-9842-f4592030b4f1","order_by":1,"name":"Lingming Meng","email":"","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Lingming","middleName":"","lastName":"Meng","suffix":""},{"id":593386676,"identity":"6e29045f-a35e-4ad3-ba8a-608a39c6d95a","order_by":2,"name":"Anchang Li","email":"","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Anchang","middleName":"","lastName":"Li","suffix":""},{"id":593386677,"identity":"a6d7c3fd-8205-421c-86e9-f5a5121effd0","order_by":3,"name":"Peng Yan","email":"","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yan","suffix":""},{"id":593386678,"identity":"a00d7b58-3090-4640-80a3-c90ca538e143","order_by":4,"name":"Yuntao Zhao","email":"","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yuntao","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-02-09 13:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8831053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8831053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103224150,"identity":"4595c51e-8b07-4c37-aa2d-595b4481ac72","added_by":"auto","created_at":"2026-02-23 10:42:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":805816,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8831053/v1_covered_bd0cc8dc-5fbc-47cb-9323-40cdef8a5b70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transmission Line Defect Detection via an Integrated Improved YOLOv8 and Deep Neural Random Forest Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aerial vehicle, SDC, Deep neural decision forest, Hybrid loss function, Multi-scale recognition","lastPublishedDoi":"10.21203/rs.3.rs-8831053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8831053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHow to effectively identify defects in power transmission lines using unmanned aerial vehicle (UAV) technology remains a key research focus. 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