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

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 article, a novel method was proposed to detect insulators on 500-kV 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 110-, 220-, and 500-kV electrical power towers along transmission pipelines; and 3) a semantic segmentation network to identify insulators on the detected 500-kV 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 four regions. Experimental 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

Automated summary

This article proposes a novel method for detecting insulators on 500-kV transmission towers in remote sensing images. The method consists of three components: a super-resolution network, an object detection model, and a semantic segmentation network. Experimental results show that the proposed method effectively detects insulators in high-resolution satellite images and achieves the highest F1 score of 0.7952.