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.
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