Gabor-YOLONet: A lightweight and efficient detection network for low-voltage power lines from unmanned aerial vehicle images

Abstract

Maintaining and monitoring low-voltage overhead power lines are of great importance because such lines are the key link between the power system and low-voltage power users. At present, few networks can be detected accurately on intelligent edge identification devices because of the complex backgrounds and limited characteristics in unmanned aerial vehicle images as well as the low computing abilities of hardware. In order to give consideration to accuracy and speed, a novel power line detection method was proposed, denoted by Gabor-YOLONet, used for intelligent edge identification devices available to UAV. Unlike existing methods, the proposed method uses the Gabor algorithm to extract foreground of power lines from cluttered backgrounds automatically and predict power lines and their auxiliary targets such as insulators in the foreground scene. In addition, a new inference method was introduced, which can summarize the average location and orientation of auxiliary targets by clustering to verify the rationality of the predicted results for power lines. The experiment results showed that the proposed method had the higher accuracy and consumed less computing resources; compared with other methods, the mAP of identification for power lines was 86.6% and the running time was only 25 ms, with excellent performance on intelligent edge devices.