Rapid detection of incomplete coal and gangue based on improved PSPNet

• A fast recognition network of coals and gangues was proposed. • Feature fusion channels and an attention mechanism are embedded in this network. • A lightweight feature extraction module was built to improve recognition accuracy. • A three-layer pyramid module is built to extract multi-scale features of targets. • Our method can solve the problem of low recognition rate in a complex environment. Aiming at the rapid identification of coal and gangue under multi-scale, adhesion, and half-occlusion conditions, a semantic segmentation network of coal and gangue image (SSNet_CG) based on the pyramid scene parsing network(PSPNet) is proposed. Firstly, the backbone feature extraction network of PSPNet is optimized. For the one, the attention mechanism is embedded in the inverted residual block (IRB) to strengthen the detailed feature information of coal and gangue in image; for another, depthwise separable convolution (DSC) and atrous convolution (AC) are used to replace the typical convolution to reduce parameters. Subsequently, the number of feature levels in the original pyramid pooling module (PPM) are reduced to minimize parameters. Finally, two feature fusion channels are added to refine the coal and gangue segmentation boundary in the adhesive state. Compared with some classic recognition models, the results show that our method has the best effects, the MPA, mIoU and F1_scores are respectively 97.3, 95.4 and 0.98, and the single image test time is 0.027 s. This method can accurately identify multi-scale and partially blocked coals and gangues.