Research on Transmission Line Insulator Defects Detection using YOLOv7

Abstract

Electrical component inspection has been a significant problem in the power distribution system. If not thoroughly investigated, poorly connected transmission lines could result in catastrophic power outages or blackouts. Much research on transmission line insulators, transformers, cables, twisted conductors, electric poles, and other power grid equipment and parts has been conducted to prevent such failures. In this work, a total of 1975 insulator images are used as the dataset, and an image augmentation technique is used to overcome the data insufficiency problem. This article’s main contribution is applying the recently proposed YOLOv7 single-stage object detector and its variants for accurately and efficiently detecting outdoor insulator defects. The experimental results show that the object detectors successfully identify insulator string as the primary class and three other subclasses, such as flashover damage, good, and broken insulator shells. The detection accuracy of the YOLOv7x object detector, among different YOLOv7 variants, can reach up to 97.5% with proper hyperparameter tuning.