A Bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging

Abstract

The precise classification of coal and gangue is a crucial link for effective sorting and efficient utilization. However, there are some shortcomings in traditional methods, such as water consumption, coal slime pollution, and great influence of environmental factors, and so on. Here, multispectral imaging technology combined with the convolutional neural network (CNN) was applied to classify coal and gangue, in which the hyperparameters of the CNN model were optimized by Bayesian algorithm. The multispectral images in the range of 675–975 nm of 209 pieces of coal and 201 pieces of gangue, which came from the Huainan mining area, were collected. The CNN and traditional modeling methods (combination strategy of image feature extraction and classifier) were employed to develop identification models, and the classification results were analyzed and compared on the multispectral dataset of coal and gangue. The identification analysis model based on CNN had the best performance, and the F1 score reached 1.00. At this time, the hyperparameters of the model are as follows: network depth was 1, initial learning rate was 0.012939, random gradient descent momentum was 0.83813, and L2 regularization intensity was 0.0099852. Moreover, the robustness of the CNN identification model was verified by introducing different levels of noise signals. The identification analysis model based on the CNN can quickly and accurately identify coal and gangue without complex image processing steps, and the model has certain anti-interference ability, which will promote the progress of automatic separation technology for coal and gangue.