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Dec. 2022
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Yunzuo Zhang, Kaina Guo. Power Plant Indicator Light Detection System Based on Improved YOLOv5[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 605-612. doi: 10.15918/j.jbit1004-0579.2022.079
Citation: Yunzuo Zhang, Kaina Guo. Power Plant Indicator Light Detection System Based on Improved YOLOv5[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 605-612.doi:10.15918/j.jbit1004-0579.2022.079
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Power Plant Indicator Light Detection System Based on Improved YOLOv5

doi:10.15918/j.jbit1004-0579.2022.079

  • School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Funds:This work was supported by the National Natural Science Foundation of China (Nos. 61702347, 62027801), the Natural Science Foundation of Hebei Province (Nos. F2022210007, F2017210161), the Science and Technology Project of Hebei Education Department (Nos. ZD2022100, QN2017132), and the Central Guidance on Local Science and Technology Development Fund (No.226Z0501G).
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  • Author Bio:

    Yunzuo Zhangreceived the B.S. degree from School of Information Science and Engineering, Hebei University of Science and Technology, Hebei in 2007, the M.S. degree from School of Information and Communication, Guilin University of Electronic Science and Technology, Guilin, in 2011 and the Ph.D. degree from School of Information and Electronics, Beijing Institute of Technology, Beijing, in 2016. In 2018, he was a visiting scholar in California State University. He is currently a Professor with School of Information Science and Technology, Shijiazhuang Tiedao University, Hebei, China. His research interests include video processing, image processing and radar signal processing

    Kaina Guoreceived the B.S. degree in software engineering from the Shijiazhuang University of Computer Science and Engineering, in 2018. She is currently a graduate research candidate with the Computer Science and Technology of Shijiazhuang Tiedao University. Her main interests include image processing, surveillance video analysis and processing

  • Corresponding author:zhangyunzuo888@sina.com
  • Received Date:2022-07-15
  • Rev Recd Date:2022-09-05
  • Accepted Date:2022-09-10
  • Publish Date:2022-12-25
    Abstract
  • Electricity plays a vital role in daily life and economic development. The status of the indicator lights of the power plant needs to be checked regularly to ensure the normal supply of electricity. Aiming at the problem of a large amount of data and different sizes of indicator light detection, we propose an improved You Only Look Once vision 5 (YOLOv5) power plant indicator light detection algorithm. The algorithm improves the feature extraction ability based on YOLOv5s. First, our algorithm enhances the ability of the network to perceive small objects by combining attention modules for multi-scale feature extraction. Second, we adjust the loss function to ensure the stability of the object frame during the regression process and improve the convergence accuracy. Finally, transfer learning is used to augment the dataset to improve the robustness of the algorithm. The experimental results show that the average accuracy of the proposed squeeze-and-excitation YOLOv5s (SE-YOLOv5s) algorithm is increased by 4.39% to 95.31% compared with the YOLOv5s algorithm. The proposed algorithm can better meet the engineering needs of power plant indicator light detection.
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