Recognition method of coal and gangue based on multispectral spectral characteristics combined with one-dimensional convolutional neural network - Hu, Feng; Zhou, Mengran; Dai, Rongying; Liu, Yu (Frontiers in Earth Science, 2022)
Accurate identification of coal and gangue is very important to realize efficient separation of coal and gangue and clean utilization of coal. Therefore, a method for identifying coal and gangue by using multi-spectral spectral information and CNN model is proposed. Firstly, 200 pieces of coal and 200 pieces of gangue in Huainan mining area were collected as the experimental materials. The multispectral information of coal and gangue was collected, and the average value of each wavelength position was calculated to obtain the spectral information of the whole band. Then, based on the one-dimensional convolutional neural network (1D-CNN), namely 1D-CNN-A and 1D-CNN-B, and with the help of SGD, Adam, Adamax and Nadam optimizers, the ReLU function and its improved function were used as the activation function to compare the identification ability of the identification models with different network structures and parameters. The best 1D-CNN model for the identification of coal and gangue based on multispectral spectral information is obtained as follows: a network model containing three one-dimensional convolution units B, PReLU is used as the activation function, and Nadam is used as an optimizer to achieve the best identification effect. At this time, 40 coal samples in the test set can be accurately identified, and only one gangue sample in 40 gangue samples is wrongly predicted as coal. Finally, compared with the traditional recognition strategy, the excellent performance of this method is further proved. The results show that the combination of multispectral imaging and 1D-CNN can achieve accurate identification of coal and gangue without considering how to select appropriate preprocessing and feature extraction methods, which is of great significance to promote the development of separation technology for coal and gangue.