Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet

Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet | 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 Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet Chuanyang Liu, Jingjing Liu, Yujuan Lin, Yiquan Wu, Zuo Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3974987/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 As an indispensable part of transmission lines, metal fittings play an important role in the safe and stable operation of power system. Automatic detection of metal fittings in the aerial images of power inspection is an urgent problem to be solved. In this study, a deep learning method is proposed to perform metal fittings detection in UAV-based images. Firstly, metal fittings data set used for targets detection is created, including Damper, XC Clamp, and NZ Clamp. Secondly, a new deep learning model is proposed for metal fittings detection. To further improve the accuracy of metal fittings identification in aerial images, ResNet50, attention mechanism, BiFPN and Focal loss are adopted to improve RetinaNet model. Finally, the proposed model and comparative models are trained and tested on metal fittings data set. Experimental results show that the proposed model has a good effect on metal fittings detection in aerial images from power inspection. More importantly, the mAP of the proposed model can reach 93.22%, which is 7%, 9.5%, 16%, and 0.4% higher than that of original RetinaNet, Faster R-CNN, SSD and YOLOv7, respectively. And the AP values of Damper, XC Clamp and NZ Clamp detection can reach 99%, 96%, and 85% by the proposed model. In addition, compared with the mainstream target detection models, the proposed model can obtain better performance in the detection of metal fittings with complex backgrounds and small targets. metal fittings target detection deep learning aerial image attention mechanism 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. 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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-3974987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274212463,"identity":"019f8fc1-9aca-4553-99d5-916d341601b9","order_by":0,"name":"Chuanyang Liu","email":"data:image/png;base64,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","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chuanyang","middleName":"","lastName":"Liu","suffix":""},{"id":274212464,"identity":"774c8180-4196-489f-830b-6ddf89fc0f32","order_by":1,"name":"Jingjing Liu","email":"","orcid":"","institution":"Chizhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Liu","suffix":""},{"id":274212465,"identity":"9e88e446-f8cf-4eb2-86ac-d96d2632bce1","order_by":2,"name":"Yujuan Lin","email":"","orcid":"","institution":"State grid Anhui Electric power Co., LTD, Ultra High Voltage Company","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yujuan","middleName":"","lastName":"Lin","suffix":""},{"id":274212466,"identity":"ce02f30b-cf49-422c-a3cc-24f79dd95b68","order_by":3,"name":"Yiquan Wu","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiquan","middleName":"","lastName":"Wu","suffix":""},{"id":274212467,"identity":"e2daf024-5b95-400d-8293-dd161fa99b13","order_by":4,"name":"Zuo Sun","email":"","orcid":"","institution":"Chizhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zuo","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-02-21 08:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3974987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3974987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53495372,"identity":"9cd4145d-e0b8-4f47-906f-1f0a133fc84f","added_by":"auto","created_at":"2024-03-26 16:39:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5506940,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonidentificationofmetalfittingsintransmissionlinesbasedonimprovedRetinaNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3974987/v1_covered_548a28a0-543a-4df3-aeed-197efce9d87d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet","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":"metal fittings, target detection, deep learning, aerial image, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-3974987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3974987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As an indispensable part of transmission lines, metal fittings play an important role in the safe and stable operation of power system. Automatic detection of metal fittings in the aerial images of power inspection is an urgent problem to be solved. In this study, a deep learning method is proposed to perform metal fittings detection in UAV-based images. Firstly, metal fittings data set used for targets detection is created, including Damper, XC Clamp, and NZ Clamp. Secondly, a new deep learning model is proposed for metal fittings detection. To further improve the accuracy of metal fittings identification in aerial images, ResNet50, attention mechanism, BiFPN and Focal loss are adopted to improve RetinaNet model. Finally, the proposed model and comparative models are trained and tested on metal fittings data set. Experimental results show that the proposed model has a good effect on metal fittings detection in aerial images from power inspection. More importantly, the mAP of the proposed model can reach 93.22%, which is 7%, 9.5%, 16%, and 0.4% higher than that of original RetinaNet, Faster R-CNN, SSD and YOLOv7, respectively. And the AP values of Damper, XC Clamp and NZ Clamp detection can reach 99%, 96%, and 85% by the proposed model. In addition, compared with the mainstream target detection models, the proposed model can obtain better performance in the detection of metal fittings with complex backgrounds and small targets.","manuscriptTitle":"Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 05:48:58","doi":"10.21203/rs.3.rs-3974987/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":"ab82fdc2-25cd-4e80-a409-3e04f5fadd96","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T16:30:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 05:48:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3974987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3974987","identity":"rs-3974987","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}