Laser & Optoelectronics Progress, Volume. 59, Issue 4, 0415003(2022)

Unlabeled Video Retrieval Method of Mining Personnel Based on MK-YOLOV4

Yunhui Zhao1, Xiaozhou Cheng2, Kaiwen Dong1、*, Xiao Yun1, Yanjing Sun1, and Yingjie Han1
Author Affiliations
  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou , Jiangsu 221116, China
  • 2Sinosteel Maanshan Institute of Mining Research Co., Ltd., Maanshan, Anhui 243000, China
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    Figures & Tables(11)
    MK-YOLOV4 network structure
    Flowchart of anchor box generation method
    CA-NET structure
    Data enhancement effect. (a) Color jitter; (b) random erasure
    Some examples of Miner-Market personnel re-identification data set
    Algorithm performance comparison
    Detection results in actual mining scene
    Weight coefficient β analysis. (a) mAP; (b) Rank1
    Mining staff re-identification search results
    • Table 1. Comparison of different network performance

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      Table 1. Comparison of different network performance

      BackbonemAP /%Rank1 /%
      ResNet502365.682.7
      SeResNet502068.484.5
      ResNet50-IBN-a1967.083.9
      CA-NET69.085.6
    • Table 2. Performance analysis of different models in Miner-Market data set

      View table

      Table 2. Performance analysis of different models in Miner-Market data set

      ModelmAP /%Rank1 /%
      CA-NET+Trihardwc loss+Color jitter78.691.2
      CA-NET+Trihardwc loss+Color jitter+REA82.493.0
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    Yunhui Zhao, Xiaozhou Cheng, Kaiwen Dong, Xiao Yun, Yanjing Sun, Yingjie Han. Unlabeled Video Retrieval Method of Mining Personnel Based on MK-YOLOV4[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0415003

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    Paper Information

    Category: Machine Vision

    Received: Mar. 1, 2021

    Accepted: Apr. 2, 2021

    Published Online: Feb. 15, 2022

    The Author Email: Kaiwen Dong (dongkaiwen1996@outlook.com)

    DOI:10.3788/LOP202259.0415003

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