SYFLo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines

SYFLo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines | 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 SYFLo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines Raja Sekhar Sankuri, Nagesh Bhattu Sristy, Sri Phani Krishna Karri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4584579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 11 You are reading this latest preprint version Abstract Sustainable transmission of electrical energy to consumers across regions relies heavily on the integrity of power transmission lines and continuous monitoring of assets is crucial for maintaining system reliability. Unmanned Aerial Vehicles (UAVs) have revolutionized defect identification in real-time and accessibility, even in difficult-to-reach geographical landscapes, thereby improving image-based inspections. This work introduces SYFLo(Semisupervised Yolo with Focal Loss function), a novel method that augments YOLO for real-time health monitoring of electric assets in power transmission lines. SYFLo integrates the focal loss function with semi-supervised learning to effectively address the lack of abundant labeled data, data imbalances and enhance detection accuracy. Additionally, it improves data generalizability across a wide range of images, ensuring robust performance despite varied image backgrounds. By leveraging YOLOv8, SYFLo significantly improves fault identification, achieving a detection accuracy of 96.5% and an FPS of 283.2. Experimental results demonstrate the impact of the proposed approach, highlighting its potential to enhance the reliability of power transmission line monitoring. These findings underscore the importance of integrating advanced deep learning techniques with innovative loss functions to address common challenges in real-time health monitoring systems. Deep learning Power grid inspection Defect detection Semi-supervised learning Focal loss function Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 02 Sep, 2024 Reviews received at journal 01 Sep, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 01 Aug, 2024 Reviewers agreed at journal 04 Jul, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers invited by journal 19 Jun, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 14 Jun, 2024 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. 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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-4584579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321800879,"identity":"2c88d0f4-047f-4666-9be5-77d3c21f4ea3","order_by":0,"name":"Raja Sekhar Sankuri","email":"","orcid":"","institution":"National Institute of Technology Andhra Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"Sekhar","lastName":"Sankuri","suffix":""},{"id":321800880,"identity":"9ddd71c5-8745-435f-b120-e74a36f41cf2","order_by":1,"name":"Nagesh Bhattu Sristy","email":"","orcid":"","institution":"National Institute of Technology Andhra Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Nagesh","middleName":"Bhattu","lastName":"Sristy","suffix":""},{"id":321800881,"identity":"a9d0e0a2-a633-4796-aacd-a369e09b08eb","order_by":2,"name":"Sri Phani Krishna Karri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYFACxgZmIMnDD2QAaeYEqLAEYS2SDYyNDURqASoDEQYHwNbAteAG8rMPtz0uqLkjY3y7uf3Bzx3WeebtBxg//GCwyMOlxeBcYrvxjGPPeMzuHGxs7D2TXixzJoFZsodBohinFh7GNmketsM8ZjcSGxt42w4nzmBIYJAG+iWxAZfDekBa/h3mMZ6R2Nj4F6SF/wHzb3xaGM4AtQAN5zGQSGxsBtsikcCG1xaDM4ztxrx9h3kkgA6bLduWDtTysM2yxwCfw9ifPeb5dtief0b6g49v26yBDks+fONHRR1uhzEwsKELgOLHALd6bFpGwSgYBaNgFKACAAC7VpPqYeh4AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology Andhra Pradesh","correspondingAuthor":true,"prefix":"","firstName":"Sri","middleName":"Phani Krishna","lastName":"Karri","suffix":""}],"badges":[],"createdAt":"2024-06-15 02:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4584579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4584579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11554-024-01566-x","type":"published","date":"2024-11-30T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382797,"identity":"7df2e304-279d-40e3-81c0-7ee4d18b7442","added_by":"auto","created_at":"2024-12-02 16:31:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":764730,"visible":true,"origin":"","legend":"","description":"","filename":"UpdatedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4584579/v1_covered_6a73e4bb-54cb-4ebf-b77e-5c8b03c4cc5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSYFLo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-real-time-image-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rtip","sideBox":"Learn more about [Journal of Real-Time Image Processing](http://link.springer.com/journal/11554)","snPcode":"11554","submissionUrl":"https://submission.nature.com/new-submission/11554/3","title":"Journal of Real-Time Image Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, Power grid inspection, Defect detection, Semi-supervised learning, Focal loss function","lastPublishedDoi":"10.21203/rs.3.rs-4584579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4584579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSustainable transmission of electrical energy to consumers across regions relies heavily on the integrity of power transmission lines and continuous monitoring of assets is crucial for maintaining system reliability. 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