露天煤矿采场地形复杂，车辆事故时有发生，准确定位车辆位置对于安全生产至关重要。针对矿区无人机遥感图像小目标定位不准确的问题，提出了一种改进的YOLOv7目标检测模型。首先对原始网络模型中的ELAN模块进行卷积替换，加速网络的推理速度。并在此基础上，进一步结合eSE通道注意力机制，形成PConv-eSE卷积注意力模块，加强模型网络对小目标的特征提取能力，降低背景信息的影响。最后，使用NWD度量标准的损失函数，进一步优化网络，提高准确性。在矿区采场车辆数据集上对改进的模型进行了实验验证，结果表明： 改进后的模型Pmav值达到94.5%，相对于原始模型上升了7.2%，有效解决了原始网络对于遥感小目标定位漏检的问题，为无人机在露天矿区小目标定位领域的应用提供了理论基础。

Open-pit coal mine stopes witness frequent vehicle accidents due to their complex terrains. Therefore, accurately positioning vehicles in the stopes is critical to safe coal mining. To overcome the inaccurate positioning of small targets in the mining areas using Unmanned aerial vehicle (UAV) remote sensing images, this study proposed an improved YOLOv7 model for object detection. First, to expedite the reasoning of the YOLOv7 network, the ELAN module in the original YOLOv7 model was improved by introducing partial convolution. Based on this, the eSE channel attention mechanism was combined to form the PConv-eSE convolution attention module. The purpose is to enhance the network’s ability to extract the features of small targets and reduce the impact of background information. Finally, the loss function of the Normalized Wasserstein Distance (NWD) metric was used to further optimize the network and improve the accuracy. The improved YOLOv7 model was verified through experiments using a vehicle dataset of a mining area stope. The results show that the Pmav value of the improved YOLOv7 model reached 94.5%, which was 7.2% higher than the original model, thus effectively overcoming the missing of small targets from remote sensing images in the detection using the original network. This study will provide a theoretical basis for the positioning of small objects in an open-pit coal mining area using UAVs.