Rapid detection of incomplete coal and gangue based on improved PSPNet

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.

Automated summary

In this study, a semantic segmentation network called SSNet_CG based on the PSPNet is proposed to rapidly identify coal and gangue under multi-scale, adhesion, and half-occlusion conditions. The method optimizes the backbone feature extraction network, embeds attention mechanisms, substitutes typical convolutions with depthwise separable and atrous convolutions, reduces the number of feature levels, and adds feature fusion channels. Experimental results show that the proposed method achieves the best effects with high accuracy and fast processing time in identifying multi-scale and partially blocked coals and gangues.