A coal and gangue detection method for low light and dusty environments

Abstract

Abstract In practical underground production environments, low light intensity and the presence of dust can disrupt the recognition of coal and gangue. To advance the separation of coal and gangue in underground settings, this paper introduces a coal–gangue recognition approach (DG Module (DGM) + YOLOX-PSB) tailored for conditions of low light intensity and dust. To address the impact of dusty conditions, a preprocessing module (DGM) is proposed. This module employs a dark channel prior dehazing algorithm to mitigate the impact of fog on coal–gangue images. Subsequent steps include white balancing, bilateral filtering, and gamma correction to alleviate noise and distortion issues arising from the dehazing algorithm. To counteract potential drawbacks of the DGM and enhance target recognition accuracy, a polarized self-attention mechanism is integrated during the feature extraction stage to prioritize edge information of coal–gangue targets. By combining the attributes of the weighted bidirectional feature pyramid network, multiple layers of coal–gangue features are efficiently fused to achieve precise identification of coal–gangue targets. Experimental results using a custom dataset demonstrate that the enhanced algorithm outperforms YOLOv3, YOLOv5, YOLOv7-Tiny, and YOLOX, achieving a recognition accuracy of 97.6%, a frames per second rate of 99 and a good smoke concentration robustness. The proposed DGM + YOLOX-PSB serves as a valuable reference for accurate coal and gangue identification in conditions of low light intensity and dust within underground environments.