Coal-gangue image classification method

For problems that traditional coal-gangue separation methods such as manual separation method, mechanical wet-separation method, γ-ray separation method and so on could not give consideration to high efficiency, safety and easy operation, a coal-gangue image classification method based on machine vision was proposed. Coal-gangue image is pre-processed with enhancement, smoothing and denoising, then segmented and extracted by watershed algorithm based on distance conversion. HOG feature and gray-level co-occurrence matrix of the coal-gangue image are selected, and coal-gangue classification based on feature extraction is carried out by taking support vector machine, random forest and K-nearest neighbor algorithm as classifier separately. Coal-gangue image classification based on convolutional neural network is carried out by building shallow-level convolutional neural network and VGG16 network pre-trained by ImageNet dataset separately. The research results show that the maximum accuracy rate of the coal-gangue image classification method based on VGG16 is 99.7%, which is higher than that of the method based on feature extraction with 91.9% or the method based on shallow convolutional neural network with 92.5%.