针对无人机航拍图像中背景复杂、小目标样本多，难以提取有效特征等问题，提出一种改进实时Transformer（real-time detection Transformer, RT-DETR）的无人机航拍小目标检测算法。首先，在特征融合网络中增加针对微小目标的特征融合结构，利用浅层特征图中丰富的位置信息来增强网络对小目标的检测能力，同时为了防止额外参数的增加，去除主干网络中最后一个残差结构；其次，设计一种多通道特征部分卷积模块（multichannel partial convolution, MCPConv），基于此重新构造了主干网络中的BasicBlock结构，命名为MCP Block，减少通道特征冗余，提升多尺度细节特征的获取能力；引入具有学习能力的位置编码，获取更精确、更具表达能力的位置信息；最后引入归一化加权偏差（normalized weighted deviation, NWD）和平均精度驱动交并比（mean precision-driven IoU, MPDIoU）定位损失函数，降低对位置偏差的敏感性，加快模型收敛速度。实验结果表明，在VisDrone2019-DET数据集上，改进后的模型较原始模型参数量降低了62%，检测精度mAP50提升了3.9%，且FPS较改进前提升了17%，对比其他主流检测模型具有更好的检测效果。

An algorithm has been proposed to detect small targets in unmanned aerial vehicle(UAV) aerial images. The algorithm is based on an improved real-time detection Transformer (RT-DETR) and aims to address the challenges posed by complex backgrounds and a large number of small target samples. To enhance the feature fusion network, a dedicated feature fusion structure for small targets has been incorporated, utilizing rich location information from the shallow feature map to improve the network's ability to detect small targets. Furthermore, the last residual block in the BackBone has been removed to prevent an increase in additional parameters. Additionally, the MCP Block, a reconstructed BasicBlock structure in the backbone network, has been designed, which includes a multi-channel feature partial convolution module (MCPConv) to reduce redundancy in channel features and enhance the acquisition of multi-scale detail features. Moreover, a location encoding mechanism with learning ability has been introduced to obtain more accurate and expressive location information. The normalized weighted deviation (NWD) and mean precision-driven IoU(MPDIoU) positioning loss functions have been incorporated to accelerate the convergence speed of the model and reduce sensitivity to position deviation. Experimental results on the VisDrone2019-DET dataset demonstrate that the improved model reduces parameters by 62% compared to the original model, increases mAP50 by 3.9%, and improves FPS by 17%. The improved model exhibits superior detection performance compared to other mainstream detection models.