Volume 48 Issue 11
Nov.  2022
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WANG Wenxin, HUANG Jie, WANG Xiuyu, et al. X-ray transmission intelligent coal-gangue recognition method[J]. Journal of Mine Automation,2022,48(11):27-32, 62.  doi: 10.13272/j.issn.1671-251x.18037
Citation: WANG Wenxin, HUANG Jie, WANG Xiuyu, et al. X-ray transmission intelligent coal-gangue recognition method[J]. Journal of Mine Automation,2022,48(11):27-32, 62.  doi: 10.13272/j.issn.1671-251x.18037

X-ray transmission intelligent coal-gangue recognition method

doi: 10.13272/j.issn.1671-251x.18037
  • Received Date: 2022-09-01
  • Rev Recd Date: 2022-11-09
  • Available Online: 2022-11-17
  • The coal-gangue image recognition is an important part of coal-gangue separation technology based on pseudo dual energy X-ray transmission (XRT). However, it is difficult to segment the coal-gangue image due to the close proximity or occlusion of coal-gangue, and it is easy to cause classification and recognition errors of coal-gangue based on artificial threshold discrimination. Due to the above influence, existing coal-gangue recognition methods have low precision. In this paper, an X-ray transmission intelligent coal-gangue recognition method is proposed. A U-Net model combined with the receptive field block (RFB) is used to realize the effective segmentation of the pseudo dual energy X-ray coal-gangue image, which is termed as RFB + U-Net model. The problem that the recognition precision is affected by the close proximity or shielding of coal-gangue is solved. The recognition features of coal-gangue are the minimum gray value of the low-energy image in the gray level features of coal-gangue image, and the minimum value and the average difference of sharpened low-energy image in the texture features. A multi layer perceptron (MLP) model is used to realize coal-gangue recognition. Experimental results show that the RFB+U-Net model is superior to the active contour model, U-Net model and SegNet model in terms of coal-gangue segmentation accuracy, coal-gangue particle size precision, coal-gangue pixel mean intersection ratio and image segmentation effect. The reasoning time of the model is short, meeting the real-time requirements of coal-gangue image segmentation. When the number of hidden layers in the MLP model is 8, the average coal-gangue recognition accuracy under two test sets is more than 87%. Under the same data set and experimental conditions, the average recognition accuracy and gangue removal rate of the MLP model are higher than those based on Bayesian classifier, support vector machine, logic regression, decision tree, gradient boosting decision tree and K-nearest neighbor algorithm. The coal carrying rate of gangue shall not exceed 3%, meeting the requirements of actual dry coal-gangue separation.

     

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