Addressing challenges in UAV-based pedestrian detection, including small target sizes, complex backgrounds, and computational constraints of resource-limited devices, this study proposes an improved pedestrian detection algorithm, CGDRF-YOLO. First, the algorithm proposes a downsampling module to aggregate multi-scale receptive field and context information, which mitigates feature loss and strengthens anti-interference capabilities. Secondly, a content-aware feature reassembling operator is integrated to dynamically generate adaptive kernels and reorganize features, thus enhancing feature utilization efficiency. Finally, the Inner-WIoU loss function is incorporated to improve localization precision and optimize loss calculation. Experimental results from the VisDrone2019 pedestrian detection sub-dataset demonstrate that, compared with the baseline algorithm, CGDRF-YOLO not only improves mAP50, mAP50:95 and FPS by 3.6%, 2.2% and 5%, but also reduces the FLOPs and Params by 10% and 5%, respectively. This clearly demonstrates that CGDRF-YOLO can effectively meet the requirements of pedestrian detection tasks in UAV scenarios.