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 $mA{P}_{50}$ 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 $mA{P}_{50}$ 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.

In unmanned aerial vehicle (UAV)-based small object detection, enhancing object perception under complex backgrounds remains a critical challenge for current detection models. Owing to the small scale, low pixel occupancy, and cluttered backgrounds of UAV small objects, their discriminative features are prone to attenuation in deep networks, which limits multi-scale feature fusion and consequently increases detection difficulty. To resolve these issues, this paper presents a Detail-Aware Multi-scale Fusion Network (DMF-Net) for UAV small object detection, consisting of two core modules: the Multi-Branch Detail-Enhanced Module (MBDE) and the Dual-Attention Fusion (DAF) Module. First, during feature extraction, a multi-branch detail enhancement module with serial convolutions in each branch and residual connections is introduced to strengthen high-frequency details and local textures while preserving semantic consistency. Second, at the feature fusion stage, a dual-attention-based feature fusion module with a symmetric interaction structure is designed to dynamically evaluate the significance of features at different scales via adaptive attention mechanisms, enabling symmetric cross-scale interaction and fine-grained feature complementarity. The experimental results obtained on the challenging VisDrone and TinyPerson datasets confirm that DMF-Net outperforms existing state-of-the-art detection methods in terms of the accuracy of small object detection, while maintaining high inference efficiency. On the TinyPerson dataset, DMF-Net improves AP by 1.4%, AP_50 by 4.1%, and AP_75 by 0.6% compared with YOLO11n, while maintaining 97.2 FPS. Furthermore, it shows promising performance in complex backgrounds and densely populated scenarios.