在煤矿带式输送物料过程中，异物的出现可能会引发输送带撕裂或堵塞等安全风险。针对输送带输送物料中异物多样、人工巡检效率低、硬件限制等问题，提出一种基于改进YOLOv8n的轻量化煤矿带式输送异物检测算法：采用GhostNetV2网络对原CSPDarkNet53主干网络进行轻量化改进，以减少模型的参数和计算量；整合全局平均池化和全局最大池化思想优化SPPF模块，关注煤矿恶劣环境影响下图像的底层信息；设计了headC2f_CA模块，融入通道注意力机制，以便能够更有效地捕捉不同尺度和位置的异物特征，强化特征信息表达；引入DIoU损失函数，精确反映锚框与预测框之间的相似度，提升模型检测精度。实验结果表明，改进后的模型平均精度均值达88.3%，相比于基线模型YOLOv8n，提升了0.8%，参数量减少了18.51%，计算量减小了20.73%，模型大小缩减了15.87%。该模型有效缓解了边缘设备的硬件限制，同时保障了煤矿安全监测的准确性。

During the coal mine belt conveyor process, the presence of foreign objects may cause safety risks such as conveying belt tearing or blockage.Aiming at the problems such as various foreign objects in conveyor belt materials, low efficiency of manual inspection and hardware limitations, a lightweight coal mine belt conveyor foreign object detection algorithm based on improved YOLOv8n was proposed. The original CSPDarkNet53 backbone network is lightweight improved by using GhostNetV2 network to reduce the parameters and calculation of the model. The SPPF module is optimized by integrating the ideas of global average pooling and global maximum pooling, and pays attention to the underlying information of the image under the harsh environmental impact of coal mines. The headC2f_CA module is designed to integrate the channel attention mechanism, so as to capture foreign object features in different scales and positions more effectively and enhance the expression of feature information.DIoU loss function is introduced to accurately reflect the similarity between anchor frame and prediction frame, and improve the detection accuracy of the model.The results show that the average accuracy of the improved model is 88.3%, which is 0.8% higher than that of the baseline model YOLOv8n. The number of parameters is reduced by 18.51%. The computational cost is reduced by 20.73%, and the model size is reduced by 15.87%. The model not only effectively alleviates the hardware limitations of edge devices, but also ensures the accuracy of coal mine safety monitoring.