Research on visual features and identification of coal and gangue in simulated underground mine environments

Abstract

ABSTRACT In underground mine environments, factors such as low light intensity, diffusion of dust particles, and coal slurry coverage interfere with coal gangue identification. To further realize coal-gangue sorting underground, we analyzed the coal-gangue visual characteristics coupled with the above factors. The analysis reveals that both smoke environments and coal slurry coverage tend to make the coal-gangue visual features more consistent. There are still differences in coal-gangue visual features under smoke environments, while coal slurry coverage leads to gangue exhibiting coal-related feature information. To improve the detection capability of coal gangue in underground environments, this study adopts a coal gangue recognition method based on the DG module and YOLOX-PSB network: Utilize the DG module to achieve dehazing and then use the YOLOX-PSB model for object detection. The proposed method was validated through ablation experiments, and the results showed that the detection accuracy reached 99.6%, with an FPS of 98. The proposed method enables real-time detection and exhibits the best robustness in complex environments, realizing reliable and efficient performance.