Volume 47 Issue 12
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YE Ou, DOU Xiaoyi, FU Yan, DENG Jun. Coal block detection method integrating lightweight network and dual attention mechanism[J]. Journal of Mine Automation, 2021, 47(12): 75-80. DOI: 10.13272/j.issn.1671-251x.2021030075
Citation: YE Ou, DOU Xiaoyi, FU Yan, DENG Jun. Coal block detection method integrating lightweight network and dual attention mechanism[J]. Journal of Mine Automation, 2021, 47(12): 75-80. DOI: 10.13272/j.issn.1671-251x.2021030075
shu

Coal block detection method integrating lightweight network and dual attention mechanism

  • 1.

    College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China;

  • 2.

    College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

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  • Received Date: March 22, 2021
  • Revised Date: December 18, 2021
    Abstract
  • In order to solve the problems of low detection precision and slow detection speed of existing coal block detection methods on belt conveyor in underground coal mine, an improved YOLOv4 model integrating lightweight network and dual attention mechanism is proposed, and it is applied to coal block detection of belt conveyor. The improved YOLOv4 model uses K-means clustering algorithm to re-cluster the prior frames, so that the prior frames are more suitable for the size of the detected target. The model improves the backbone network structure by introducing the MobileNet lightweight network model to reduce the amount of model parameters and calculations, and improve the detection speed. A convolution block attention module with dual attention mechanism is embedded to improve the sensitivity of the model to target characteristics, suppress interference information and improve the precision of target detection. The experimental results show that the improved YOLOv4 model can detect coal blocks of different sizes accurately. Compared with the YOLOv4 model, the improved YOLOv4 model weight file is reduced by 36.46%, the accuracy rate is increased by 2.16%, the recall rate is increased by 20.4%, the average accuracy is increased by 14.37%, the missed detection rate is decreased by 16%, the detection speed is increased by 19 frames/s, the processing time for a single image is reduced by 1.31 s, which improves the detection precision and speed of coal block detection.
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    • References (17)
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    DENG Jun
    • 1.

      College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China;

    • 2.

      College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

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