RDDR-YOLO: Real-Time Lightweight Human Detection for UAV-Based Surveillance

Abstract

Driven by the rapid advancement of un-manned aerial vehicle (UAV) and deep learning technologies, UAV surveillance has emerged as a vital supplement to conventional ground monitoring systems due to its flexible deployment, wide coverage, and real-time capability. However, most state-of-the-art human action recognition models are trained on ground-level video data and struggle to adapt to the inherent characteristics of aerial imagery, including extremely small object scales, low resolution, complex background interference, and severe mutual occlusion. To address these technical bottlenecks in UAV-based human detection, this paper presents a lightweight yet high-precision detection method, dubbed RDDR-YOLO, based on YOLOv8s. Extensive experiments are conducted on two public benchmark datasets, VisDrone2019 and TinyPerson. Results show that RDDR-YOLO achieves mAP50 values of 43.9% and 36.4% on the two datasets, outperforming the YOLOv8s baseline by 7.0% and 9.6%, respectively. Its human localization precision reaches 41.2%, yielding an improvement of 14.5%. Moreover, the parameter count is reduced by 67.6%, and the inference speed reaches 70 FPS. The proposed method substantially improves the detection accuracy of small-scale human targets in UAV scenarios while maintaining competitive real-time performance. It can serve as the 2D localization module in the first stage of a two-stage human action recognition framework, effectively supporting human pose estimation and motion capture in UAV-based scenarios. The method holds significant practical potential in smart city security, emergency search and rescue, crowd management, and other related fields.