Enhancing Real-Time Object Detection With Optical Flow-Guided Streaming Perception

Real-time object detection in Unmanned Aerial Vehicle (UAV) videos remains a significant challenge due to the fast motion and small scale of objects. Existing streaming perception models struggle to accurately capture fine-grained motion cues between consecutive frames, leading to suboptimal performance in dynamic UAV scenarios. To address these challenges, StreamFlow is proposed to integrate optical flow information and enhance real-time object detection in UAV videos. StreamFlow incorporates Flow-Guided Dynamic Prediction (FGDP) to refine position predictions using local optical flow information and Optical Flow Guided Optimization (OFGO) to optimize model parameters considering both localization loss and optical flow reliability. Central to OFGO is the Adaptive Flow Weighting (AFW) module, which focuses on reliable flow samples during training. The proposed integration of optical flow and adaptive weighting scheme significantly enhances the ability of streaming perception models to handle fast-moving objects in dynamic UAV environments. Extensive experiments on four challenging UAV video datasets demonstrate the superior performance of StreamFlow compared to state-of-the-art methods in terms of accuracy.