Volume 49 Issue 11
Nov.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(11): 60-66
WU Jiangwei, NAN Bingfei. Method for recognizing coal flow status of scraper conveyor in working face[J]. Journal of Mine Automation，2023，49（11）：60-66.  doi: 10.13272/j.issn.1671-251x.2023080101
Citation: WU Jiangwei, NAN Bingfei. Method for recognizing coal flow status of scraper conveyor in working face[J]. Journal of Mine Automation，2023，49（11）：60-66.  doi: 10.13272/j.issn.1671-251x.2023080101
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Method for recognizing coal flow status of scraper conveyor in working face

doi: 10.13272/j.issn.1671-251x.2023080101

  • CCTEG Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101300, China

  • Received Date: 2023-08-28
  • Rev Recd Date: 2023-11-19
    • Available Online: 2023-11-27
    Abstract
  • The various poses of scraper conveyors, irregular coal material shapes, limited equipment installation positions, high dust, and foreign object obstruction in the scene of scraper conveyors in underground coal mines have led to the inability of existing coal flow status recognition methods for belt conveyor scenarios to be applied in engineering. In order to solve the above problems, a method for recognizing the coal flow status of a scraper conveyor in a working face based on temporal visual features is proposed. This method first utilizes the DeepLabV3+semantic segmentation model to obtain rough coal flow regions in the coal flow video image of the working face. Then the method uses linear fitting method to locate and segment fine coal flow regions, achieving coal flow image extraction. Then the method arranges the coal flow images in video sequence to form a sequence of coal flow images. Finally, a convolutional 3D (C3D) action recognition model is used to model the features of coal flow image sequences and achieve automatic recognition of coal flow status. The experimental results show that this method can accurately obtain coal flow images and automatically and real-time recognize coal flow status, with an average recognition accuracy of 92.73% for coal flow status. For engineering deployment applications, TensorRT is used to accelerate model processing. For the coal flow video image with a resolution of 1 280×720, the overall processing speed is 42.7 frames/s, which meets the actual demand for intelligent monitoring of coal flow status at the working face.

     

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