Muhammad Iqbal AmerruddinMuhammad Fakhrur RaziIzzuddin Mat LazimKhairul Nabilah Zainul AriffinHafizal MohamadLiyana Ramli2025-10-092025-10-092025Muhammad Iqbal Amerruddin, Muhammad Fakhrur Razii Izzuddin Mat Lazim,Khairul Nabilah Zainul Ariffin,Hafizal Mohamadi Liyana Ramli. (2025). Autonomous UAV-Based Power Transmission Line-Guided Inspection Using Yolo Deep Learning. Seminar Antarabangsa Islam Dan Sains 2025 (SAIS 2025), 191–206. https://drive.google.com/drive/folders/1SN6JaJp4OjO-505O0GGa06Ro1JnzHAeH3083-872Xhttps://drive.google.com/drive/folders/1SN6JaJp4OjO-505O0GGa06Ro1JnzHAeHhttps://oarep.usim.edu.my/handle/123456789/27720Seminar Antarabangsa Islam dan Sains 2025 (SAIS 2025) / “Transcending Generations: Naqli and Aqli asPillars of Ummah Transformation”. Editor: Siti Rubaini Mat , Umi Hamidaton Mohd Soffian Lee, Nur Ilyana Ismarau Tajuddin, Noorfajri Ismail, Azman Ab Rahman 9 September 2024 Anjuran: Persatuan Kakitangan Akademik Universiti Sains Islam Malaysia (PKAUSIM),Fakulti Syariah dan Undang-Undang (FSU), Institut Fatwa dan Halal (iFFAH), USIMThe continuous monitoring and inspection of power transmission lines are vital to ensure the stability and reliability of the power grid. Traditional inspection methods are time-consuming, costly, and pose significant safety risks to workers. This research presents an autonomous Unmanned Aerial Vehicle (UAV)-based system for power transmission line inspection, utilizing advanced deep learning techniques, specifically the YOLOv11 model, to enhance fault detection and navigation accuracy. The proposed system integrates a YOLOv11 object detection model to identify power lines from UAV-captured footage and employs a proportional control navigation algorithm to autonomously guide the UAV along the transmission lines. The model was trained on a diverse dataset of powerline imagery to ensure robustness across varying environmental conditions. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) demonstrated the model’s ability to accurately detect power lines with high confidence. The developed system effectively tracks power lines in video, offering a significant improvement over traditional inspection methods. This approach not only reduces operational costs and risks but also increases the efficiency and accuracy of power line inspections. The results highlight the potential of integrating UAVs and deep learning for autonomous infrastructure maintenance in critical power transmission systems.en-USUAVpower transmission linesYOLOv11deep learninginspection.text::conference output::conference proceedings::conference paper191206