{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-object-detection-on-high-voltage","title":"Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)","arxiv_id":null,"date":"2023-08-30","proceeding":"58th International Universities Power Engineering Conference (UPEC) 2023 8","authors":["Elisavet Bellou","Ioana Pisica","Konstantinos Banitsas"],"abstract":"Unmanned Aerial Vehicles (UAVs) are gaining significant scientific interest in critical infrastructure inspection due to their flexibility, cost-effectiveness and advanced computer vision capabilities. This research focuses on high-voltage powerline surveillance, where automatic inspection is a priority for grid companies to prevent power failures. To address the need for real-time detection with limited computational power, we evaluate the recently developed object detection algorithm, YOLOvS. We propose a fine-tuned model trained on a custom dataset to detect key components, i. e. towers, insulators and conductors. The proposed method achieves an overall accuracy rate of 82.3% (mAp@O.S) and enables real-time detection, demonstrating its suitability for inspection tasks and visual-based navigation. Our model was also tested on a custom-built quadcopter with an Nvidia Jetson Nano (4GB) on board, achieving a frame rate of 33fps on live video under real environmental conditions.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10294447","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10294447","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-object-detection-on-high-voltage","repo_url":"https://github.com/Elizbellou/Powerline-Detection-with-YOLOv5-and-Jetson-Nano","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}