<p>Comparison experiment of loss functions.</p>

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.针对输送带因大块煤炭引发的设备堵塞与皮带损坏问题，本研究提出了基于YOLOv8n的轻量级实时检测模型SCCG-YOLO。该模型在主干网络中引入CPNGhost模块，以提升大目标的感受野覆盖范围与边缘细节提取能力；在特征融合阶段集成Shuffle Attention注意力机制，以改善复杂光照与粉尘干扰下的特征辨识度；将颈部网络中的固定上采样替换为CARAFE模块，以优化高层语义特征的重构效果；并采用DIoU损失函数，强化边界框回归过程中的几何约束。本研究在公开数据集CUMT-Belt的任务专用衍生子集上开展实验，经图像筛选、标签校正与重新标注后，共保留1276张有效图像，并按照8:1:1的比例划分为训练集、验证集与测试集。实验结果表明，SCCG-YOLO的mAP@50达到91.9%，帧率为532.6 FPS，参数量仅为2.7 MB，在检测精度、运行效率与模型紧凑性之间实现了良好的平衡。上述结果显示，所提方法能够满足井下输送带作业的实时检测需求，对智能矿山安全预警具有实际应用价值。