Insulator Detection for High-Resolution Satellite Images Based on Deep Learning

Abstract

The detection of electrical insulators in Unmanned Aerial Vehicle (UAV) images using deep learning has made great progress in recent years, but little research has been conducted in the same field in remote sensing (RS) images. In this paper, a novel method was proposed to detect insulators on 500kV transmission towers in RS images. The proposed method consists of three components including 1) a super-resolution (SR) network to improve image resolution, 2) an object detection model to detect 110kV, 220kV and 500kV electrical power towers along transmission pipelines and 3) a semantic segmentation network to identify insulators on the detected 500kV towers. In addition, the online hard example mining (OHEM) method and class weight calculation method were utilized to handle the imbalanced data among different classes during training. The proposed model was evaluated on SuperView-1 and WorldView-3 satellite images collected in 4 regions. Experiment results show that the proposed method can effectively detect insulators in high-resolution satellite images and achieved the highest F1 score of 0.7952. The codes are available at https://github.com/hardworking-jws/insulator-detection-remote-sensing.