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To solve the problem of online detection of coal-carrying rate in gangue, an approach based on particle queuing and binocular machine vision was proposed. First, a queuing device was applied to line up the coal and gangue particles. A binocular camera was used to obtain images of coal and gangue from above the belt. Through image segmentation, the coal and gangue particle regions were divided from the belt background. Then, the Semi-Global Block Matching (SGBM) algorithm was used to obtain the height information matrix of each region. At the same time, the projected area of the particles was calculated from the area of the divided regions. It was converted from pixel units to physical units by capturing an image of a ruler. The height matrix and projected area were combined to construct a mechanism-based volume model by integral principle. Finally, the calculated volume of coal and gangue were multiplied by their respective empirical density to get the mass of particles. The coal-carrying rate in gangue was obtained by dividing the mass of coal by the total mass. The average relative error of the proposed method is 2.79%. It not only overcomes the prediction error caused by particle stacking, but also improves the computational efficiency of image processing, and the “white-box model” is also easy to understand.

Detecting coal-carrying rate in gangue based on binocular machine vision and particle queuing method