Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection

원문 초록

Object detection from Unmanned Aerial Vehicles (UAVs) is challenged by severe ego-motion, camera jitter, and large scale variations. While modern detectors perform well on static images, their direct application to UAV video often fails, particularly for small objects in dynamic scenes. Existing motion-based methods often rely on computationally expensive optical flow. We propose a vision-only motion-guided detection framework that decouples target motion from camera-induced disturbances. Homography-based Global Motion Compensation (GMC) first aligns adjacent frames. We then introduce a Dual-Interval Motion Extraction strategy that captures both short-term and long-term motion cues. To integrate these cues, a lightweight Dual-Interval Motion Injection (DIMI) module enhances feature representations within a Feature Pyramid Network. Experiments on the VisDrone-VID dataset demonstrate consistent improvements over the YOLOv8n baseline under severe ego-motion. Ablation studies further confirm the effectiveness of the dual-interval design and DIMI.