An Accurate and Real-time Method of Self-blast Glass Insulator Location Based on Faster R-CNN and U-net with Aerial Images

This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-noise-ratio(SNR)setting.We deal with two modules:1)object detection based on Faster R-CNN,and 2)classification of pixels based on U-net.For the first time,our paper combines the above two modules.This combination is motivated as follows:Faster R-CNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach.Furthermore,a comparison is made among different methods and the result shows that our approach is accurate in real time.