Lightweight GB-YOLOv5m State Detection Method for Power Switchgear

CUI Haoyang; YANG Kexin; GE Haihua; XU Yongpeng; WANG Haoran; YANG Cheng; DAI Yingying

doi:10.11999/JEIT220288

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3777-3787

CUI Haoyang, YANG Kexin, GE Haihua, XU Yongpeng, WANG Haoran, YANG Cheng, DAI Yingying. Lightweight GB-YOLOv5m State Detection Method for Power Switchgear[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288

Citation:

CUI Haoyang, YANG Kexin, GE Haihua, XU Yongpeng, WANG Haoran, YANG Cheng, DAI Yingying. Lightweight GB-YOLOv5m State Detection Method for Power Switchgear[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288

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Lightweight GB-YOLOv5m State Detection Method for Power Switchgear

doi: 10.11999/JEIT220288

1.
The College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2.
Shanghai Zhabei Power Plant, Shanghai 200438, China
3.
School of Electronic Information and Electrical Engineering , Shanghai Jiao Tong University, Shanghai 200241, China

Funds: The National Natural Science Foundation of China (52177185)

Received Date: 2022-03-16
Accepted Date: 2022-07-14
Rev Recd Date: 2022-07-06

Available Online: 2022-07-21

Publish Date: 2022-11-14

Abstract

Abstract

Since status lights and instruments of power switchgear have the characteristics of high density and ectopic image, the detection ability of basic features such as target morphology, chromaticity comparison, and lightweight recognition ability in edge image processing technology presents higher requirements. Therefore, a Ghost-Bifpn-YOLOv5m(GB-YOLOv5m) method is proposed. Specifically, the Bi-directional Feature Pyramid Network (BiFPN) structure is adopted to give different weights to the feature layer to transmit more effective feature information. A detection layer scale is added to improve the detection accuracy of the network for small targets and tackle the complication of small target recognition caused by the high-density layout of status lights. The Ghost-Bottleneck structure is employed to replace the complex Bottleneck structure of the original backbone and realize the lightweight of the model, contributing to the formation of favorable conditions for deploying the model at the edge. Additionally, the transmission characteristics of status lights and instruments for limited samples are expanded with the image enhancement technology, and the high-speed convergence is realized through migration learning. The experimental results of 10 kV switchgear demonstrate that the algorithm has high recognition accuracy for 16 categories of cabinet status lights and instruments, with mean Average Precision (mAP) of 97.3% and fps of 37.533. Compared with the YOLOv5m algorithm, the model size is reduced by 37.04%, and mAP is increased by 10.2%, implying that the proposed method possesses a significantly enhanced detection ability for lamp bodies and table bodies, as well as remarkably improved lightweight recognition efficiency, which has certain practical significance for the real-time verification of the power state of the switchgear and the interaction of digital twins.
- Small target detection,
- Lightweight,
- YOLOv5,
- Instrument and status light detection

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References(21)

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