Welcome to Journal of Graphics share: 

Journal of Graphics ›› 2023, Vol. 44 ›› Issue (3): 448-455.DOI: 10.11996/JG.j.2095-302X.2023030448

Previous Articles     Next Articles

Improved substation instrument target detection method for YOLOv5 algorithm

MAO Ai-kun(), LIU Xin-ming(), CHEN Wen-zhuang, SONG Shao-lou   

  1. Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2022-08-20 Accepted:2023-01-04 Online:2023-06-30 Published:2023-06-30
  • Contact: LIU Xin-ming (1984-), lecturer, Ph.D. His main research interests cover complex industrial modeling, optimization and diagnosis. E-mail:83660832@qq.com
  • About author:

    MAO Ai-kun (1997-), master student, His main research interest covesr deep learning of power machine vision. E-mail:2398494063@qq.com

  • Supported by:
    Scientific Research Fund project of Liaoning Provincial Department of Education(LJY013)


An efficient and accurate edge instrument detection equipment is crucial for building intelligent substations. However, due to the complex environment of substation, it is challenging for mobile side devices to quickly and accurately detect small targets, multi-categories, and highly similar instrument targets. To address this, a power instrument target detection method based on lightweight SS-YOLOv5 network was proposed. The algorithm was built on YOLOv5 and utilized the lightweight network ShuffleNet V2 to improve the model’s network structure. Deep separable convolutions were introduced to extract instrument features, reducing the computational complexity of the model during neck fusion and improving detection speed. Additionally, combined with Swin Transformer, modeling was undertaken through shift window, allowing for global and local information interaction and enhancing feature extraction ability. Finally, the image data set of substation instrument was constructed independently to train, test, and verify the model. The experimental results demonstrated that, compared with the YOLOv5 algorithm, the model parameters were reduced by 91.8% and the target detection speed was increased by 43.8% for the detection of pressure gauge, ammeter, voltmeter, and other instrument images. It provided a feasible technical scheme for deployment to the side, and could accelerate the development of substation informatization and intelligence.

Key words: deep learning, substation instruments, YOLOv5, target detection, lightweight network

CLC Number: