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.