A collaborative multi-attention network for real-time small object detection in UAV imagery

To address the challenges of detecting small objects in unmanned aerial vehicle (UAV) imagery, such as weak feature representation, complex background interference, and high real-time requirements, this paper proposes a Collaborative Multi-Attention Network (CMA-Net) for real-time small object detection. The network incorporates an efficient bi-directional feature pyramid structure (E-BiFPN) to achieve multi-scale weighted feature fusion while minimizing parameter count and computational cost. A Dual-Dimensional Channel Attention (DDCA) module is further introduced, which adaptively recalibrates channel significance along the width and height dimensions, capturing long-range dependencies and improving spatial sensitivity. Additionally, a Multi-Scale Foreground Attention (MSFA) module is designed to explore inter-object correlations across different feature layers, enhancing foreground representation, suppressing background interference, and improving feature discriminability for small objects. By integrating E-BiFPN, DDCA, and MSFA, CMA-Net achieves collaborative feature enhancement and significantly boosts overall discriminative power. Experimental results demonstrate that the proposed method achieves accuracies of 67.2% and 62.0% on the public UAVDT and Stanford Drone datasets, respectively, while operating at 64 frames per second, meeting real-time inference requirements.