针对遥感图像小目标众多、目标检测器参数量大和检测效率低等问题，提出了一种改进的YOLOv7-tiny的轻量级遥感图像小目标检测模型。首先，针对原始模型中跨阶段局部空间金字塔池化网络复杂的碎片化操作，提出轻量级的空间金字塔池化结构来减少多余的卷积算子操作；其次，针对颈部网络冗余的模块化连接方式和小目标容易在深层特征丢失空间信息的问题，提出深层语义信息引导的单尺度预测头方法来进行小目标位置信息强化，并进一步减少颈部网络和头部网络的计算成本。在遥感图像数据集上展开实验，结果表明，改进后的模型比原始模型参数量降低49.6%，计算复杂度降低28.5%，推理速度提高73.1%，并优于现阶段其他主流轻量级目标检测器。

Aiming at the problems of numerous small targets in remote sensing images, large number of target detector parameters and low detection efficiency, an improved lightweight remote sensing image small target detection model of YOLOv7-tiny was proposed. First, to address the complex fragmentation operations of the cross-stage local spatial pyramidal pooling network in the original model, a lightweight spatial pyramidal pooling structure was proposed to reduce the redundant convolution operator operations. Second, to address the problems of redundant modular connectivity of the neck network and the easy loss of spatial information of small targets in deep features, a single-scale prediction head method guided by deep semantic information was proposed to reduce the neck network and head network to reduce the computational cost of the neck network and head network. Experiments were carried out on remote sensing image datasets, and the results show that the improved model reduces the number of parameters by 49.6%, computational complexity by 28.5%, and inference speed by 73.1% compared with the original model, and outperforms other mainstream lightweight target detectors at this stage.