Abstract
To improve the efficiency of coal and gangue separation in coal production, it is imaging based on double-ray transmission minerals and uses image processing and a lightweight neural network model. Now, a lightweight neural network model called SAB-MobileNet, namely Speed and Accuracy Balance MobileNet is proposed to efficiently identify and classify coal and gangue samples. Based on the MobileNetV2 network, the SAB-MobileNet lightweight network model improves the sampling, integrates the attention mechanism and the change of the cascade network structure, can realize the high accuracy of maintaining the coal and gangue classification tasks, greatly reduces the computational amount and parameters of the network model, and improves the efficiency of the overall identification and classification. The SAB-MobileNet network model achieved 98.4% accuracy in coal and gangue classification, greatly reduced the number of parameters and calculation, and greatly improved the efficiency of coal and gangue classification, indicating the feasibility and practicability of the model for the coal and gangue classification task.