{"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/aerial-inspection-of-high-voltage-power-lines","title":"Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector","arxiv_id":null,"date":"2024-05-24","proceeding":"Energies 2024 5","authors":["Elisavet Bellou","Ioana Pisica","Konstantinos Banitsas"],"abstract":"The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8’s architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model’s pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5).","url_abs":"https://www.mdpi.com/1996-1073/17/11/2535","url_pdf":"https://www.mdpi.com/1996-1073/17/11/2535","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":"aerial-inspection-of-high-voltage-power-lines","repo_url":"https://github.com/Elizbellou/Powerline-TIC-Dataset-and-Detection-Models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"defect-detection","task_name":"Defect Detection"},{"task_slug":"line-detection","task_name":"Line Detection"}],"methods":[{"method_slug":"yolov8","method_name":"YOLOv8"}],"datasets_introduced":[{"slug":"tic-powerline-dataset","name":"TIC Powerline Dataset","full_name":"Tower Insulator Conductors Dataset for high voltage powerline inspection"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}