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Aiming at the problem that the identification of coal and gangue is easily affected by external factors, a coal gangue identification method based on multi-dimensional gradient features and gray texture features is proposed. Research shows that Sobel operators with different window sizes have different degrees of interest in image textures. The larger the window, the more interested they are in fine textures. The characteristic curves obtained by different Sobel operators are quite different, and the optimal gradient features can be obtained by using various Sobel operators. The separability of gradient features is higher than that of gray texture features, and the gradient features are less affected by light. The classification model established in this paper has a recognition rate of over 97% for different mining areas, and the detection time of a single image is less than 20.5 ms. When the light intensity changes from 600 lux to 1800 lux, the fluctuation range of the  F1 -score in this method is only 0.26%. The fusion of multi-dimensional gradient features and gray texture features can significantly reduce the influence of external factors on the recognition model, and improve the recognition accuracy and anti-interference ability of the model.

Recognition of coal and gangue based on multi-dimensional gray gradient feature fusion