DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images

Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. In this paper, we propose DAU-YOLO, a novel object detection method tailored for drone imagery, built upon the YOLOv11 framework. To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. In the neck, we propose a Dynamic Attention and Upsampling (DAU) module, which incorporates additional low-level features rich in small-object information. Furthermore, Scale-Diffusion Attention and Task-Aware Attention are employed to refine these features, significantly improving the network’s ability to detect small objects. To maintain an extremely lightweight architecture, the bottom-most Bottom–Up layer is removed, reducing model complexity without compromising detection accuracy. In the experiments, the proposed method achieves state-of-the-art (SOTA) performance on the VisDrone2019 dataset. On the validation set, DAU-YOLO(l) attains an mAP50 of 56.1%, surpassing the baseline YOLOv11(l) by 9.1% and the latest similar-structure method Drone-YOLO(l) by 4.8%, while maintaining only 28.9M parameters, almost half those of Drone-YOLO(l). In the discussion, we provide a detailed analysis of the improvements in small object detection as well as the trade-off between detection accuracy and inference speed. These results demonstrate the effectiveness of DAU-YOLO in addressing the challenges of drone object detection, offering a highly accurate and lightweight solution for real-time applications in complex aerial scenes.