Surface Defect Detection of Power Equipment Using Adaptive Receptive Field Network

Authors

Hao Yu, School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, ChinaFollow
Jinxia Jiang, Information & Telecommunication Branch, State Grid Zhejiang Electric Power Co. , Ltd. , Hangzhou 310020, China
Xiaohan Lai, Information & Telecommunication Branch, State Grid Zhejiang Electric Power Co. , Ltd. , Hangzhou 310020, China
Feng Mei, Information & Telecommunication Branch, State Grid Zhejiang Electric Power Co. , Ltd. , Hangzhou 310020, China

Abstract

Abstract: For the detection of defects such as icing, rust, and contamination of power equipment in substations, a novel adaptive receptive field network (ARFN) is proposed, in which an adaptive receptive field module (ARFM) combined with the attention mechanism can effectively fuse multi-scale features. Considering the small sample learning attribute of defect detection, a power equipment surface defect simulation data synthesis method based on real texture is also proposed. The experimental results on the simulation dataset show that the network has high detection accuracy for surface defects across devices, while having advantages such as small size and fast operation speed.

Recommended Citation

Yu, Hao; Jiang, Jinxia; Lai, Xiaohan; and Mei, Feng (2023) "Surface Defect Detection of Power Equipment Using Adaptive Receptive Field Network," Journal of System Simulation: Vol. 35: Iss. 7, Article 14.
DOI: 10.16182/j.issn1004731x.joss.22-0278
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss7/14

First Page

1572

Last Page

1580

CLC

TP391.9

Recommended Citation

Yu Hao, Jiang Jinxia, Lai Xiaohan, et al. Surface Defect Detection of Power Equipment Using Adaptive Receptive Field Network[J]. Journal of System Simulation, 2023, 35(7): 1572-1580.

Corresponding Author

Qing Wang, qwang@nwpu.edu.cn

DOI

10.16182/j.issn1004731x.joss.22-0278