Papers › Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)
Elisavet Bellou, Ioana Pisica, Konstantinos Banitsas
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.
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