Volume 48 Issue 11
Nov.  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(11): 33-38
CHEN Biao, LU Zhaolin, DAI Wei, et al. Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model[J]. Journal of Mine Automation，2022，48(11)：33-38.  doi: 10.13272/j.issn.1671-251x.18035
Citation: CHEN Biao, LU Zhaolin, DAI Wei, et al. Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model[J]. Journal of Mine Automation，2022，48(11)：33-38.  doi: 10.13272/j.issn.1671-251x.18035
shu

Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model

doi: 10.13272/j.issn.1671-251x.18035
  • 1.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China

  • 2.

    Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China

  • 3.

    Dadi Engineering Development (Group) Co., Ltd., Tianjin 300000, China

  • 4.

    Guodian Jiantou Inner Mongolia Energy Investment Co., Ltd., Ordos 017209, China

  • Received Date: 2022-09-27
  • Rev Recd Date: 2022-11-05
    • Available Online: 2022-11-17
    Abstract
  • The existing coal-gangue separation methods based on vision technology have problems of large model parameter amount, poor feature extraction capability and low recognition precision. In order to solve the above problems, a coal-gangue recognition method based on YOLOX-S model combined lightweight Ghost-S network and hybrid parallel attention module (HPAM) named HPG-YOLOS-S model is proposed. Firstly, HPAM is added to the backbone network of YOLOX-S model. Thus the important information in an image is enhanced, and the secondary information is inhibited. The feature extraction capability of the backbone network is enhanced. Secondly, the backbone network of YOLOX-S model is replaced by Ghost-S network with smaller parameter quantity. The utilization rate and feature fusion capability are improved. Finally, in the predection layer, the SIOU loss function is used to replace the loss function of YOLOX-S model to impsrove the detection and positioning precision and enhance the extraction capability of the target. In order to verify the detection effect of the proposed method on large coal-gangue, the HPG-YOLOX-S model is compared with YOLOX-S model. The results show that the identification accuracy of the HPG-YOLOX-S model for coal and gangue is 99.53% and 99.60% respectively, which is 2.51% and 1.27% higher than those of YOLOX-S model. The results of validation show that the precision rate, recall rate and F1 value of the HPG-YOLOX-S model are all above 94%, which are 5.68%, 3.51% and 2.91% higher than those of YOLOX-S model respectively. The parameters amount of the HPG-YOLOX-S model is 7.8 MB, which is 1.2 MB lower than that of YOLOX-S model. The ablation experiment results show that the mean average precision of the HPG-YOLOX-S model is 9.17% higher than that of YOLOX-S model. The experiment result of visualization of the thermodynamic diagram shows that the HPG-YOLOX-S model focuses on the surface differences between coal and gangue, such as texture and contour. The model pays more attention to the overall target of coal-gangue.

     

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