Visual inspection of transmission line defects by unmanned aerial vehicles based on convolution algorithm and deep forest network

Visual inspection of transmission line defects by unmanned aerial vehicles based on convolution algorithm and deep forest 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 Article Visual inspection of transmission line defects by unmanned aerial vehicles based on convolution algorithm and deep forest network Liangshuai Liu, Lingming Meng, Anchang Li, Peng Yan, Yuntao Zhao This is a preprint; it has not been peer reviewed by a journal.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-7478584/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 development of unmanned aerial vehicle technology provides a feasible solution for detecting transmission lines in complex terrain. How to effectively identify defects in power transmission lines in combination with unmanned aerial vehicle technology is the focus of research. The paper is optimized based on the traditional convolutional neural network, and a spatially deformable convolutional (SDC) algorithm is proposed to extract information from the images captured by unmanned aerial vehicles, and then features are fused based on the deep neural decision forest. At the same time, a more comprehensive hybrid loss function is proposed to further improve the model's recognition of multi-scale defect images. The instance verification is found that the model's recognition accuracy for the five types of normal, stains, cracks, corrosion and surface peeling is all above 92%. To determine the functions of each module, ablation experiments are conducted. When extracting image features using SDC, the mPA is increased to 90.48%. When the deep neural decision forest is adopted as the feature fusion method, the mPA is increased to 91.5%. When training with the loss function and then recognizing different images, the mPA is increased to 86.71%. By combining the three algorithms, in the detection of five different categories of transmission lines, the PA values are respectively increased by 7.86, 9.34, 8.95, 6.01 and 10.05. The mPA has been increased by 8.44%. The model is compared with several traditional recognition models. Compared with Faster R-CNN and YOLOv7, the detection speed is increased by 11.63 and 2.13 respectively, and the number of involved parameters is decreased by 54.57 and 37.37% respectively. Compared with YOLOv9, although the detection speed has decreased and the number of involved parameters has increased, the mPA has been increased by 2.34% through the increase in model complexity. The proposed model can effectively identify multi-scale transmission line defects. 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: 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-7478584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511369646,"identity":"33fa182b-2d6e-49ec-ba14-a96f4dc486a9","order_by":0,"name":"Liangshuai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie2QMUvDQBTH7zh4WZ7Nekfa75ASiA7F+lE8hEzOzhcKdQm4WgT9Crp0vvCGbnYt1KEidE5BJEMGU0HE5eJY8H7DHQ/+P+79jzGP5wDpia/LMiZ4vqnqEYaBcSvwowgazoqsrwrbobBvhUEWIdAoXp11KIHYvr43L/rmGpMYcYlsxXi1u3QtBsfJYLrVt4TDjTxZI78zQs3mzi5ppAxpQ+0rMa5R9C2II6cSfESyIf1AmMpzeEZozw4FU1UB6UeCTFqwiH9QriI+peSJ2k/OiwuUWE6cXcJwMVd1Q4P7ZZm/NfXpeLyYlNXOoewR+Hvmxp3fR+rOiMfj8fxrPgHpJFAJ2vC/eAAAAABJRU5ErkJggg==","orcid":"","institution":"State Grid Hebei Electric Power Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Liangshuai","middleName":"","lastName":"Liu","suffix":""},{"id":511369647,"identity":"44686b59-e265-4339-8742-13a9041fdf32","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":511369648,"identity":"76eacbe1-a5ca-4a62-b710-2fc073d795e1","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":511369649,"identity":"ee020014-c538-4983-8750-8a460a2e1fa8","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":511369650,"identity":"132f314e-6ece-486b-a19c-2a592aef0426","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":"2025-08-28 09:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7478584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7478584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99307326,"identity":"beef6489-0a6a-4a1e-993d-404766577105","added_by":"auto","created_at":"2025-12-31 16:06:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":754548,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7478584/v1_covered_2741f99b-c7e9-4872-bca1-c16182d20acb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visual inspection of transmission line defects by unmanned aerial vehicles based on convolution algorithm and deep forest 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":"Aerial vehicle, SDC, Deep neural decision forest, Hybrid loss function, Multi-scale recognition","lastPublishedDoi":"10.21203/rs.3.rs-7478584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7478584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of unmanned aerial vehicle technology provides a feasible solution for detecting transmission lines in complex terrain. How to effectively identify defects in power transmission lines in combination with unmanned aerial vehicle technology is the focus of research. The paper is optimized based on the traditional convolutional neural network, and a spatially deformable convolutional (SDC) algorithm is proposed to extract information from the images captured by unmanned aerial vehicles, and then features are fused based on the deep neural decision forest. At the same time, a more comprehensive hybrid loss function is proposed to further improve the model's recognition of multi-scale defect images. The instance verification is found that the model's recognition accuracy for the five types of normal, stains, cracks, corrosion and surface peeling is all above 92%. To determine the functions of each module, ablation experiments are conducted. When extracting image features using SDC, the mPA is increased to 90.48%. When the deep neural decision forest is adopted as the feature fusion method, the mPA is increased to 91.5%. When training with the loss function and then recognizing different images, the mPA is increased to 86.71%. By combining the three algorithms, in the detection of five different categories of transmission lines, the PA values are respectively increased by 7.86, 9.34, 8.95, 6.01 and 10.05. The mPA has been increased by 8.44%. The model is compared with several traditional recognition models. Compared with Faster R-CNN and YOLOv7, the detection speed is increased by 11.63 and 2.13 respectively, and the number of involved parameters is decreased by 54.57 and 37.37% respectively. Compared with YOLOv9, although the detection speed has decreased and the number of involved parameters has increased, the mPA has been increased by 2.34% through the increase in model complexity. The proposed model can effectively identify multi-scale transmission line defects.\u003c/p\u003e","manuscriptTitle":"Visual inspection of transmission line defects by unmanned aerial vehicles based on convolution algorithm and deep forest network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 18:48:13","doi":"10.21203/rs.3.rs-7478584/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"c3787188-5904-4d2e-8361-eb820db764ad","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54314486,"name":"Physical sciences/Engineering"},{"id":54314487,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-22T16:54:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 18:48:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7478584","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7478584","identity":"rs-7478584","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}