Spectroscopy identification method and system for coal and gangue based on multilocation feature fusion

Visible and near-infrared (VIS-NIR) spectral technology is fast, accurate, and non-destructive, which meets the requirements of the separation of coal and gangue. However, the performance of the classification models established by the spectra collected according to the usual single-location collection methods was unsatisfactory. Simulating the production conditions of coal gangue sorting, this paper proposed a multilocation spectral feature fusion identification method for coal and gangue to improve the identification accuracy and developed an identification system. In the multilocation spectral collection method, the spectra of the sample were collected at three locations under the probe, which obtained richer information. In the classification models built based on different feature fusion methods, the one-dimensional convolutional neural network model with feature layer fusion had the strongest classification ability, with an accuracy of 97.61%, an improvement of 8.84% over the best single-location model. The regions of interest for model decision-making were visualized using the Gradient-weighted Class Activation Mapping (Grad-CAM) method to explore the reasons why the multilocation feature fusion method was more advanced. Furthermore, a coal gangue identification system was developed using LabVIEW, which can achieve fast and accurate recognition with an accuracy of 93.15% and an expenditure time of 1.241 s.