摘要： 在轻小型无人机图像目标检测任务中，常面临检测精度低、背景复杂、目标尺度变化大、分布密集以及模型参数量较大的问题，因此提出一种基于改进 RT-DETR无人机目标检测算法。首先，使用热传导模块HeatBlock和空间选择注意力模块LskBlock改进C2f得到C2f-Heat-Lsk模块，然后使用C2f-Heat-Lsk模块和C2f模块来重新设计RT-DETR主干网络，提高主干网络对小目标的特征提取能力并减少模型参数量。其次，提出特征融合结构SOFEP替代原网络的特征金字塔，缓解小目标细节信息丢失的问题，并增强小目标的特征表示。最后，结合Focaler-IoU和MPDIoU两种损失函数来构造Focaler-MPDIoU损失函数，提高边界框的回归精度进而减少模型的漏检率。实验结果表明，在VisDrone测试集上，改进模型参数量较RT-DETR降低16.9%，mAP0.5和mAP0.5:0.9指标分别提升2.6%和1.9%，在DOTAv1.0和HIT-UAV 数据集上均优于RT-DETR算法。改进模型在保持较小参数量的同时，提高了检测精度，满足了无人机航拍图像小目标检测的应用需求。

Abstract: In lightweight small UAV image object detection tasks,there are common challenges such as low detection accuracy, complex backgrounds, large variations in target scale, dense target distribution, and a relatively large number of model parameters. Therefore, this paper proposes a novel improved RT-DETR object UAV object detection algorithm. First, an enhanced C2f-Heat-Lsk module is developed through integrating the HeatBlock thermal conduction module and LskBlock spatial selective attention mechanism into the C2f structure. This modified module collaborates with the original C2f module to redesign the RT-DETR backbone network, which improves spatial feature extraction while reducing model parameters Second, a novel feature fusion structure SOFEP replaces the original feature pyramid to mitigate detail loss in small objects and enhance their feature representation. Third, a combined Focaler-MPDIoU loss function is constructed by integrating Focaler-IoU and MPDIoU loss mechanisms, which improves bounding box regression accuracy and reduces miss detection rates. Experimental results on the VisDrone test set show that the improved model reduces parameter count by 16.9% compared to RT-DETR, while achieving improvements of 2.6% in mAP0.5 and 1.9% in mAP0.5:0.9. The model also outperforms RT-DETR on the DOTAv1.0 and HIT-UAV datasets. These advancements demonstrate that the proposed method achieves higher detection accuracy with reduced computational complexity, effectively meeting the requirements for small object detection in UAV aerial images.