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 either rely on computationally expensive optical flow or use single-interval differencing, which is sensitive to jitter and limited in capturing diverse motion patterns. We propose a vision-only motion-guided detection framework that decouples target motion from camera-induced disturbances. A 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 Motion-Guided Attention (MGA) module enhances feature representations within a Feature Pyramid Network. Experiments on the VisDrone-VID dataset demonstrate consistent improvements over a strong YOLOv8 baseline under severe ego-motion. Ablation studies further confirm the effectiveness of the dual-interval design and the proposed motion-guided attention mechanism.

翻译：从无人机平台上进行目标检测面临严重的自运动、相机抖动以及尺度剧烈变化等挑战。现有检测器在静态图像上表现优异，但直接应用于无人机视频时往往失效，特别是在动态场景中的小目标检测场景下。现有基于运动的方法要么依赖计算代价高昂的光流，要么使用对抖动敏感且难以捕捉多样化运动模式的单区间差分方法。本文提出一种纯视觉引导的运动解耦检测框架，将目标运动与相机引发的扰动进行分离。首先通过基于单应性的全局运动补偿对齐相邻帧，进而提出双区间运动提取策略，同时捕获短时与长时运动线索。为整合这些线索，引入轻量级运动引导注意力模块，在特征金字塔网络中增强特征表示。在VisDrone-VID数据集上的实验表明，在严重自运动条件下，该方法相较于强基线YOLOv8实现了一致性提升。消融研究进一步验证了双区间设计及所提运动引导注意力机制的有效性。