DARA-Net: Detail-Aware Representation and Aggregation for Lightweight UAV Detection

Detecting unmanned aerial vehicles (UAVs) in complex low-altitude scenes remains challenging due to limited target details and background interference. To address these challenges, this paper proposes DARA-Net, a lightweight UAV detection framework that coordinates detail preservation, efficient feature extraction, and multiscale aggregation. Built upon YOLOv11, DARA-Net integrates adaptive window reduction (AWR) to retain informative local features during downsampling, a separable aggregation block (SAB) to improve feature representation with fewer parameters, and a directional weighted pyramid (DWP) to enhance cross-scale feature fusion. Experiments on a visible-light UAV dataset containing 25,941 images demonstrate that DARA-Net achieves 97.0% precision, 91.1% recall, 95.1% mAP@0.5, and 62.0% mAP@0.5:0.95. Compared with the YOLOv11 baseline, recall improves by 2.6 percentage points, while the parameter count decreases by approximately 30.6% and model size decreases by 29.1%, reaching 1.79 M parameters and 3.9 MB, respectively. Compared with YOLOv26n, DARA-Net achieves a higher mAP@0.5:0.95 (62.0% versus 60.77%) with approximately 24.6% fewer parameters and a 28.2% smaller model file, while maintaining comparable mAP@0.5. Additional validation on an external UAV dataset further supports its competitive detection performance. These results highlight the value of DARA-Net as a compact UAV detector that prioritizes localization performance and reduced parameter and storage requirements.