Multi-Dimensional Attention for Small Object Detection in Airport Obstacle Limitation Airspace

In airport clearance airspace, illegally flown unmanned aerial vehicles and birds in long-range imagery occupy extremely small pixel proportions, suffer from texture scarcity, and are readily overwhelmed by background clutter; consequently, generic detectors struggle to achieve accurate detection. To this end, this paper proposes a multi-dimensional attention collaborative optimization method that improves small-object detection performance across three complementary dimensions, feature enhancement, convolution decoupling, and loss reweighting. Initially, a multi-semantic self-attention module is developed, which employs multi-scale depthwise separable convolutions and group normalization to extract multi-semantic spatial features while guiding channel attention learning, thereby enhancing small-object response intensity. Subsequently, a receptive-field decoupled attention module is introduced, which decouples positions within the receptive field of standard convolution and independently assigns attention weights, thereby breaking the parameter-sharing limitation of convolution kernels and improving boundary regression sensitivity. Finally, a unified intersection-over-union loss is proposed, which adjusts the focus on predicted boxes of different qualities through dynamic scaling and annealing strategies, thereby strengthening the fine-grained refinement of high-quality boxes. Furthermore, the effectiveness of these three components is validated on four datasets: TIB, UB-SOD, Drone, and Flying. Notably, on TIB, the proposed method achieves 87.4% mAP50 and 38.9% mAP50-95, improving the baseline by 4.7% and 3.2%, respectively; moreover, it exhibits significant advantages over other methods.