Small unmanned aerial vehicle (UAV) detection in low-altitude infrared imagery remains challenging due to extremely small targets, weak contrast, scarce appearance cues, and heavy background clutter, which often leads to missed detections, clutter-induced false alarms, and localisation drift. To address these issues, we propose Directional Context Modelling DETR (DCM-DETR), an end-to-end detector that strengthens weak target evidence via directional context modelling and scale-consistent feature aggregation. Specifically, we build a Directional Receptive-Field Enhancement (DRFE) backbone with C2f-APC units, introducing asymmetric padding to enlarge receptive fields while preserving faint target cues. We further design an Infrared-Enhanced Encoder (IEE), where a CSA-Block jointly captures directional context and local details to steer global interactions towards target-relevant regions. To suppress noise propagation and alleviate cross-scale misalignment, we employ Hierarchical Gated Fusion (HGF) and Residual Alignment (RA), enabling selective semantic modulation and consistent multi-scale alignment. Moreover, we incorporate a Magnitude-Aware Linear Attention AIFI (MALA-AIFI) module to enhance low-SNR responses with linear complexity. Experiments on SIRST-UAVB show that DCM-DETR improves mAP50 by 36.58% over YOLOv8n and by 1.09% over RT-DETR, while reducing parameters by 25.1M. On IRSTD, it yields a 2.01% gain in mAP50 and boosts speed from 47.43 FPS to 93.45 FPS. These results demonstrate that DCM-DETR achieves a strong accuracy–efficiency trade-off for infrared small UAV detection in cluttered low-altitude scenes.
Li et al. (2026) studied this question.