To address equipment blockage and belt damage caused by large coal blocks on conveyor belts, this study proposes SCCG-YOLO, a lightweight real-time detection model based on YOLOv8n. The model introduces CPNGhost into the backbone to enhance receptive-field coverage and edge-detail extraction for large targets, incorporates Shuffle Attention in feature fusion to improve discriminability under complex lighting and dust interference, replaces fixed upsampling in the neck with CARAFE to refine high-level semantic reconstruction, and adopts DIoU loss to strengthen geometric constraints during bounding-box regression. Experiments were conducted on a task-specific derivative subset of the public CUMT-Belt dataset. After image screening, label correction, and re-annotation, 1,276 valid images were retained and divided into training, validation, and test sets at a ratio of 8:1:1. The results show that SCCG-YOLO achieves 91.9% mAP@50, 532.6 FPS, and only 2.7 MB parameters, demonstrating a favorable balance among detection accuracy, efficiency, and model compactness. These results indicate that the proposed method can satisfy the real-time detection requirements of underground conveyor-belt operation and has practical value for intelligent mine safety warning.
Zhan et al. (Wed,) studied this question.