Transmission Line Insulator Recognition Based on Artificial Images Data Expansion

Authors

Yaru Wang, 1.Department of Automation, North China Electric Power University, Baoding 071003, China;Follow
Kai Yang, 1.Department of Automation, North China Electric Power University, Baoding 071003, China;
Yongjie Zhai, 1.Department of Automation, North China Electric Power University, Baoding 071003, China;Follow
Congbin Guo, 1.Department of Automation, North China Electric Power University, Baoding 071003, China;
Wenqing Zhao, 2.Department of Computer, North China Electric Power University, Baoding 071003, China;
Jie Su, 1.Department of Automation, North China Electric Power University, Baoding 071003, China;

Abstract

Abstract: Deep learning method has developed rapidly in the field of computer vision, but relies on a large quantities of training data. In the task of transmission line insulator automatic detection, problems such as insufficient number of aerial insulator images and poor diversity affect the accuracy of insulator recognition. An artificial insulator images data expansion method is proposed. Artificial insulator images are created by modeling software, and a compensation network is constructed. The artificial images are compensated and optimized by compensation network, and the aerial insulator image data set is expanded by the compensated artificial insulator images. The insulator recognition experiments are carried out on several typical convolutional neural networks. The results show that the proposed method improves the accuracy of insulator identification by an average of 2.1%, and the network is relatively lightweight, which verifies the effectiveness and advantages of the proposed method.

Recommended Citation

Wang, Yaru; Yang, Kai; Zhai, Yongjie; Guo, Congbin; Zhao, Wenqing; and Su, Jie (2022) "Transmission Line Insulator Recognition Based on Artificial Images Data Expansion," Journal of System Simulation: Vol. 34: Iss. 11, Article 3.
DOI: 10.16182/j.issn1004731x.joss.21-0646
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss11/3

First Page

2337

Revised Date

2021-09-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0646

Last Page

2347

CLC

TP391.41;TP391.9

Recommended Citation

Yaru Wang, Kai Yang, Yongjie Zhai, Congbin Guo, Wenqing Zhao, Jie Su. Transmission Line Insulator Recognition Based on Artificial Images Data Expansion[J]. Journal of System Simulation, 2022, 34(11): 2337-2347.

Corresponding Author

Yongjie Zhai,zhaiyongjie@ncepu.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-0646