Volume 50 Issue 8
Aug.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(8): 91-98
CHEN Tengjie, LI Yong'an, ZHANG Zhihao, et al. Foreign object detection and counting method for belt conveyor based on improved YOLOv8n+DeepSORT[J]. Journal of Mine Automation，2024，50（8）：91-98.  doi: 10.13272/j.issn.1671-251x.2024070043
Citation: CHEN Tengjie, LI Yong'an, ZHANG Zhihao, et al. Foreign object detection and counting method for belt conveyor based on improved YOLOv8n+DeepSORT[J]. Journal of Mine Automation，2024，50（8）：91-98.  doi: 10.13272/j.issn.1671-251x.2024070043
shu

Foreign object detection and counting method for belt conveyor based on improved YOLOv8n+DeepSORT

doi: 10.13272/j.issn.1671-251x.2024070043
  • 1.

    School of Mechanical and Transportation Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • 2.

    Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China

  • 3.

    State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan University of Technology, Taiyuan 030024, China

  • 4.

    Shaqu No.1 Coal Mine, Huajin Coking Coal Co., Ltd., Lüliang 033000, China

  • Received Date: 2024-07-11
  • Rev Recd Date: 2024-08-22
    • Available Online: 2024-08-12
    Abstract
  • The existing foreign object detection methods for belt conveyors have problems such as weak capability to extract object semantic information, poor detection precision, and only recognizing and detecting foreign objects. The methods cannot accurately calculate the number of foreign objects. In order to solve the above problems, a foreign object detection and counting method for belt conveyors based on improved YOLOv8n+DeepSORT has been designed. The method improves the YOLOv8n model and then uses the improved YOLOv8n model to recognize foreign objects in belt conveyors. The method uses the foreign object detection results of the improved YOLOv8n model as input for the DeepSORT algorithm to achieve foreign object tracking and counting on belt conveyors. YOLOv8n improvement method is replacing the C2f module in the backbone network with the C2f_MLCA module to improve the network's information extraction capability in a single color information environment. The method improves the head section using the separated and enhancement attention module (SEAM) to enhance the detection precision of foreign objects when they are obstructed. The method uses Focaler IoU optimization loss function to solve the problem of large differences in the shape of detection objects. The performance verification experiment results of MSF-YOLOv8n model show that the mAP50 of MSF-YOLOv8n model reaches 93.2%, which is 2.1% higher than the basic model. The parameter count is only 2.82×106, which is 0.19×106 less than the basic model, making it more suitable for deployment in edge devices such as inspection robots. The detection precision is 2.2%, 1.3%, and 0.3% higher than YOLOv5s, YOLOv7, and YOLOv8s algorithms, respectively. Although its frame rate is lower than YOLOv8s and YOLOv8n, it still meets the requirements of real-time video detection. The results of foreign object detection and counting experiments show that the DeepSORT algorithm has an accuracy rate of 80% and can accurately track occluded anchor rods and objects with significant shape differences.

     

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