Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network

Abstract

To effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared images without considering the selection of feature extraction and classifier. Firstly, the specific architecture and detailed parameters of the model are optimized and the CNN model based on only one Inception Block contains three different convolution kernels are considered to be the most appropriate model. Next, performance of the proposed identification model is analyzed and evaluated by the infrared image dataset, and we discovered that the CNN model is capable of correctly identifying 192 training samples and 48 test samples. Finally, compared with the traditional recognition model and other CNN recognition model, it is proved that the proposed CNN model has superior recognition performance. The results state clearly that the combination of infrared image and CNN can quickly and accurately identify coal and gangue without complex image processing steps. At the same time, the model has a certain anti-interference ability for different noises. And it has a certain reference value for the research and development of intelligent coal preparation equipment.