UAV-CPD: A Benchmark Dataset with a Frequency-aware Detector for Camouflaged PersonDetection from UAV Views

Camouflaged person detection from unmanned aerial vehicle (UAV) views is important for intelligent surveillance, search and rescue, and battlefield perception, yet remains insufficiently studied. This task is particularly challenging due to small target scales, cluttered backgrounds, and large viewpoint variations in UAV imagery. Existing public datasets mainly focus on conventional pedestrian scenarios or generic camouflaged objects, and therefore cannot adequately support benchmarking for UAV-view camouflaged person detection. To address this gap, we present UAV-CPD, a benchmark dataset dedicated to camouflaged person detection from UAV views, and establish a unified evaluation protocol for this task. We further propose CamouDETR, a frequency-aware end-to-end baseline built upon Dual-Branch Wavelet Enhancement (DWE), Dynamic Frequency Routing (DFR), and the Context-Aware Reference Decoder (CARD), to strengthen weak-detail representation, cross-scale frequency transmission, and context-aware box refinement. Experiments show that CamouDETR achieves 77.30 $mAP$ on UAV-CPD, outperforming RT-DETR by 3.10 points, and also improves $mAP$ on AMAD-5 from 62.16 to 65.40. These results support UAV-CPD as a useful benchmark for this task and show that the proposed baseline offers a practical starting point for UAV-view camouflaged person detection.