An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images

CSEE Journal of Power and Energy Systems

Yuhang Zhang¹  Dongxia Zhang²  Zhijian Jin²  Xing He²  Haichun Liu²  Zenan Ling²  Robert C. Qiu³ 

[1] China Electric Power Research Institute, Beijing 100192, China;Department of Electrical Engineering, Center for Big Data and Artificial Intelligence, State Energy Smart Grid Research and Development Center, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA;

关键词: aerial images;    deep learning;    faster r-cnn;    insulators;    location;    real-time;    u-net;   

DOI  :  10.17775/CSEEJPES.2019.00460

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown