STARD-Net: SpatioTemporal Attention for Robust Detection of Tiny Airborne Objects from Moving Drones

The rapid adoption of drones across various domains, alongside advancements in computer vision, has driven growing interest in vision based airborne object detection from moving aerial platforms. However, this task remains challenging due to the small scale of objects, camouflage within cluttered backgrounds, and occlusions. To address these challenges, we introduce an end-to-end detection framework that integrates a DRFB to extract multiscale and geometrically diverse features, specifically designed to enhance the detection of small and camouflaged airborne objects. To model motion patterns over time while preserving spatial structure, particularly for detecting camouflaged, cluttered and occluded objects with limited appearance cues, we incorporate a Convolutional Long Short-Term Memory (ConvLSTM) module, which effectively captures temporal dependencies across consecutive frames. Additionally, we introduce a SpatioTemporal Attention Block (STAB), inspired by Multi-Head Attention, to aggregate spatial and temporal context for improved semantic understanding. The detection head combines a Swin Transformer with a Cross Stage Partial (CSP) Bottleneck, offering lightweight yet powerful global context modeling for robust detection in complex aerial scenes. We evaluate our model on four publicly available airborne object detection datasets from moving drones, achieving significant improvements in accuracy while maintaining real time inference speed. Moreover, when integrated into various You Look Only Once (YOLO) architectures, our spatial feature extraction module (DRFB) consistently boosts performance, demonstrating its broad applicability and effectiveness. The code is available online here.