SDS-YOLO: Drone-Based Foreign Object Detection Model for Power Lines Using an Enhanced YOLOv8n Approach

SDS-YOLO: Drone-Based Foreign Object Detection Model for Power Lines Using an Enhanced YOLOv8n Approach | 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 SDS-YOLO: Drone-Based Foreign Object Detection Model for Power Lines Using an Enhanced YOLOv8n Approach jing Sheng, Shuliang Wu, Guoman Liu, Xin Ma, Shunhu Deng, Hao Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9341531/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract To address the issues of low detection accuracy and insufficient model lightweighting in UAV-based transmission line foreign object detection, this paper proposes SDS-YOLOv8n, an optimized foreign object detection algorithm. Built upon the YOLOv8n architecture, the proposed model incorporates three targeted improvements to balance detection accuracy and computational efficiency. First, an Enhanced SPPF module integrating parallel global average and max pooling layers is designed to improve the model's focus on target edge details while suppressing environmental background interference. Second, the standard detection head is replaced with a Dynamic Detection Head that unifies scale-aware, spatial-aware, and task-aware attention mechanisms, significantly enhancing feature adaptability for irregular objects such as kites and bird nests. Third, a Parameter-free Attention Mechanism (SimAM) is embedded to further refine feature extraction without increasing model parameters. Experimental results on a self-constructed dataset demonstrate that SDS-YOLOv8n achieves a [email protected] of 95.8% and a [email protected] :0.95 of 75.1%, outperforming the baseline model by 1.1% and 2.6%, respectively. Furthermore, the model exhibits strong generalization capabilities on an additional untrained dataset. With a parameter count of 2.75 M and high inference speed verified on the NVIDIA Jetson Orin Nano platform, the proposed method provides a robust and efficient solution for real-time intelligent grid inspection. Physical sciences/Engineering Physical sciences/Mathematics and computing YOLOv8n unmanned aerial vehicle power line foreign object detection SPPF dynamic detection head attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Editor invited by journal 15 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 10 Apr, 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. 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-9341531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625926621,"identity":"659fd3cc-36dc-4001-9e41-7419b9467b64","order_by":0,"name":"jing 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vehicle, power line foreign object detection, SPPF, dynamic detection head, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-9341531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9341531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the issues of low detection accuracy and insufficient model lightweighting in UAV-based transmission line foreign object detection, this paper proposes SDS-YOLOv8n, an optimized foreign object detection algorithm. Built upon the YOLOv8n architecture, the proposed model incorporates three targeted improvements to balance detection accuracy and computational efficiency. First, an Enhanced SPPF module integrating parallel global average and max pooling layers is designed to improve the model's focus on target edge details while suppressing environmental background interference. Second, the standard detection head is replaced with a Dynamic Detection Head that unifies scale-aware, spatial-aware, and task-aware attention mechanisms, significantly enhancing feature adaptability for irregular objects such as kites and bird nests. Third, a Parameter-free Attention Mechanism (SimAM) is embedded to further refine feature extraction without increasing model parameters. Experimental results on a self-constructed dataset demonstrate that SDS-YOLOv8n achieves a [email protected] of 95.8% and a [email protected]:0.95 of 75.1%, outperforming the baseline model by 1.1% and 2.6%, respectively. 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