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Detection and recognition of digital instrument in substation using improved YOLO-v3

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Abstract

In order to monitor substation intelligently, it is of significance to obtain substation instrument automatically and accurately. This paper adopts the digital instrument of the substation in the actual scene as the research object and proposes a detection and identification method based on the improved YOLO-v3 for the substation digital instrument. In order to enrich the limited image data, this paper augments the specific image data of the number of substations collected and constructs the data set. Based on YOLO-v3, aiming at the problem of the accuracy of substation instrument detection and identification, and considering the real-time performance comprehensively, this pager proposes an improved YOLO-v3 model by using PANet structure. The effectiveness of the proposed method is verified according to the substation digital instrument detection experiment. Experimental results show that the improved YOLO-v3 is 0.23\( \% \) higher than the classical YOLO-v3 network concerning mean average precision, and it has better accuracy in substation digital instrument detection and identification. The proposed method can still guarantee a real-time performance, and the detection frames per second (FPS) of image processing is 29 f/s; it meets the actual substation intelligent data acquisition, detection and identification engineering needs.

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Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

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Author notes

  1. Huibin Shi and Zexi Hua have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Huibin Shi & Zexi Hua

  2. China Railway Guangzhou Group Co., Ltd, Guangzhou, 510088, Guangdong, China

    Jianyi Chen

  3. School of Microelectronics, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Yongchuan Tang

  4. Qianghua Times (Chengdu) Technology Co., Ltd, Chengdu, 610095, China

    Rujiang He

  5. School of Electical Engineering, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Rujiang He

Authors
  1. Huibin Shi
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  2. Zexi Hua
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  3. Jianyi Chen
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  4. Yongchuan Tang
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  5. Rujiang He
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Contributions

HS contributed to conceptualization, methodology, supervision and writing—original draft preparation. ZH was involved in conceptualization, methodology, supervision, writing—original draft preparation, and writing—reviewing and editing. JC contributed to conceptualization, data curation, validation, writing—original draft preparation, and provided software. YT was involved in conceptualization, methodology, supervision, funding, writing—original draft preparation, and writing—reviewing and editing. RH contributed to conceptualization, validation and writing—reviewing.

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Correspondence to Zexi Hua or Yongchuan Tang.

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The work was supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2023-JC-QN-0689).

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Shi, H., Hua, Z., Chen, J. et al. Detection and recognition of digital instrument in substation using improved YOLO-v3. SIViP 17, 2971–2979 (2023). https://doi.org/10.1007/s11760-023-02517-y

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