Deep learning-based detection of power transmission lines using YOLOv4 and YOLOv8

Abstract

Reliable energy distribution is considered a cornerstone of modern society, and the maintenance of power transmission lines (PTLs) plays a vital role in achieving this goal. The accurate and rapid detection of PTLs not only enhances the efficiency of energy distribution networks but also is crucial in preventing potential failures and accidents. Traditional inspection methods often rely on the human eye, leading to time-consuming, costly, and risky processes. In this context, the emergence of unmanned aerial vehicle (UAV) technology has brought about a revolutionary change in power transmission line inspections. UAVs offer unprecedented efficiency, safety, and cost-effectiveness in monitoring energy infrastructure. The integration of deep learning-based object detection methods with UAV technology enables safe and effective monitoring of power transmission lines. Particularly, the You Only Look Once (YOLO) algorithm facilitates faster and more accurate results by combining object detection and classification processes within a single network. This study comprehensively examines the object detection applications of the YOLO algorithm. The results indicate that all versions of YOLOv8 outperform YOLOv4 in terms of object detection, demonstrating high performance. The precision, recall, and F1 score performance metrics for the tested YOLOv8 versions were found to be over 99%. The average mAP50 and mAP50-95 values were found to be 0.995 and 0.919, respectively.