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

Zenan Ling; Dongxia Zhang; Robert C. Qiu; Zhijian Jin; Yuhang Zhang; Xing He; Haichun Liu

doi:10.17775/CSEEJPES.2019.00460

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

更新时间：2025-12-29

- 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 Vol. 5, Issue 4, Pages: 474-482(2019)
- 作者机构：
  
  1. Department of Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China
  2. China Electric Power Research Institute,Beijing,China
  3. Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville,TN,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.00460
  CLC：
- Published：2019
- 稿件说明：
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Zenan Ling, Dongxia Zhang, Robert C. Qiu, et al. An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images[J]. CSEE Journal of Power and Energy Systems, 2019, 5(4): 474-482.
DOI：

Zenan Ling, Dongxia Zhang, Robert C. Qiu, et al. An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images[J]. CSEE Journal of Power and Energy Systems, 2019, 5(4): 474-482. DOI： 10.17775/CSEEJPES.2019.00460.

摘要

Abstract

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.

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