Abstract
Reliable drone-to-drone perception is essential for safe autonomous operation in increasingly complex low-altitude airspace. This study proposes STDG-UAV, a lightweight spatial–temporal domain-generalized network for onboard drone-to-drone detection and collision-risk perception. The framework combines spatial tiny-target feature preservation with adaptive multi-scale fusion to enhance weak structural cues of distant UAVs. A motion-guided temporal memory module further exploits adjacent-frame information to improve detection continuity under fast motion, blur, and weak visibility. To enhance robustness to unseen environments, image-level domain randomization, feature-style perturbation, and consistency learning are introduced under a source-only training setting. Experiments show that STDG-UAV achieves 95.4% mAP@0.5, 69.8% mAP@0.5:0.95, and 64.7% APS, with only 5.8 M parameters and 14.9 GFLOPs. In continuous UAV videos, the model reaches a detection continuity rate of 96.2% and reduces the maximum consecutive missed frames to two. On unseen real-flight domains, it achieves an average mAP@0.5 of 87.6% with a generalization gap of 7.8 percentage points. Onboard experiments further demonstrate real-time inference at 28.7 FPS and accurate TTC estimation with an MAE of 0.42 s. These results demonstrate the potential of STDG-UAV for robust, real-time drone-to-drone perception and collision-risk assessment.