An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named "InSF-detection" is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the "InSF-detection". The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.