The study of coal gangue segmentation for location and shape predicts based on multispectral and improved Mask R-CNN

Abstract

The intelligent separation of gangue is of great significance to the clean and efficient utilization of coal. We propose an improved Mask R-CNN combined with multispectral imaging for coal gangue instance segmentation. Based on the lightweight of the backbone network and neck network to improve the classic mask R-CNN, recorded as L-Mask R-CNN. The positioning precision of the improved Mask R-CNN for coal and coal gangue is 96.22% and 95.12%, respectively, and the test time consumption is 6.436 s. Besides, compared to YOLO v4 and CenterNet, U-Net, and Deeplab v3+, the L-Mask R-CNN can more precisely obtain the 2D shape of each gangue instance, which allows us to evaluate its relative size. The results show that the improved L-Mask R-CNN can accurately locate the coal gangue and allows to get its relative size, which is of great significance to the intelligent separation of coal gangue.