针对矿井输送带在输送煤流的过程中，大块煤矸石和锚杆存在不同尺寸和形状，图像特征信息难以提取，传统目标检测算法检测效果不理想的问题，提出一种基于TDConv与统一注意力检测头的异物检测算法。该算法通过设计并行卷积的组合方式形成TDConv卷积模块，能有效保持图像特征原有信息，帮助更深的卷积层提取有效细节信息；在检测头部分加入统一注意力模块，有效提取和识别不同尺寸物体、不同空间位置之间的特征信息；基于煤矿井下不同场景的输送带制作了10万张矿用异物数据集(MFID)，为矿井煤流输送过程中异物检测的深入研究和实际应用提供资源支持。实验结果表明，该算法在矿用数据集MFID上的平均精度均值(mAP)与YOLOv5目标检测算法相比提升了2.1%；在具备高精度检测能力的同时，能有效减少异物检测网络模型参数量，使网络结构更加轻量化，适用于煤矿井下边缘计算设备。

In the process of coal folw transportation on mine conveyors, there are different sizes and shapes of rock bolt and large coal gangue, so it is difficult to extract image feature imformation, and the inspection effect of traditional objecet detection algorithm is not deal. To solve this problem, a foreign object detection algorithm based on TDConv and a unified attention detection head is proposed.This algorithm designs a TDConv convolution module by combining parallel convolution methods, effectively preserving the original information of image features and assisting deeper convolutional layers in extracting detailed information.An unified attention module is incorporated into the detection head to effectively extract and recognize feature information from objects of different sizes and spatial positions. A dataset of 100 000 images (MFID) of mining foreign objects was created based on various underground coal mine scenarios, providing resources for in-depth research and practical application of foreign object detection during coal flow transportation. Experimental results demonstrate that this algorithm improves the mean Average Precision (mAP) by 2.1% compared to the YOLOv5 object detection algorithm on the MFID mining dataset.Furthermore, it effectively reduces the parameter count of the foreign object detection network model while maintaining high detection accuracy, resulting in a more lightweight network structure suitable for magrinal computing devices in underground coal mines.