Research on image classification of coal and gangue based on a lightweight convolution neural network

Abstract

AbstractIt is vital to differentiate between coal and gangue in coal mining effectively. In recent years, classification methods for images of coal‐ and gangue‐based convolutional neural networks have emerged, which makes the analysis process simple and fast. However, these methods still have the limitation of balancing recognition performance and computational efficiency due to the limitation of device hardware. This research proposes a group convolution and channel shuffle augmentation Ghost network (GSAGNet) for the classification task of coal and gangue images. First, we design a new Ghost module with group convolution and channel shuffle to improve the stability of the Ghost module for better extraction of features of coal and gangue. Second, we redesign GhostNet to improve the model's classification accuracy while guaranteeing similar floating‐point operations. Finally, an efficient channel attention module is embedded in the prediction output of the model to improve the model's prediction results further. Extensive experiments on actual image datasets of coal and gangue show the feasibility and superiority of our proposed GSAG module. The GSAG module is applied to models such as AlexNet, VGG13, and ResNet50, and the accuracy of the models is improved by 0.27%, 1.24%, and 1.25%, respectively, compared to the Ghost module. The classification accuracy and F1‐score of the proposed GSAGNet model reach 97.50% and 97.50%, respectively, which are 1.01% and 1.04% higher than that of GhostNet. The GSAGNet model in this study can classify coal and gangue efficiently, which can effectively enable the separation of underground coal and gangue and has specific practical application significance and value.