通过运动引导提升小无人机在复杂场景下的检测精度
Motion-guided small MAV detection in complex and non-planar scenes
- 融合运动特征与外观特征，增强小目标检测能力
- 在ARD-MAV数据集上优于现有方法，多指标表现领先
- 适合动态复杂背景中微小型飞行器的精准识别
近年来，由于在众多应用中的重要性，微型飞行器（MAVs）的视觉检测受到广泛关注。然而，基于外观或运动特征的现有方法在背景复杂或无人机过小时难以有效工作。本文提出一种新型运动引导式小无人机检测器，可在复杂且非平面场景中准确识别小型无人机。该检测器首先利用运动特征增强模块捕捉小无人机的运动特征；随后通过多目标跟踪与轨迹滤波，消除由运动视差引起的误检；最后采用基于外观的分类器和在裁剪区域上运行的外观检测器，实现精确检测结果。所提方法通过聚合像素级运动特征，并结合运动与外观特征消除误检，能有效且高效地从动态复杂背景中检测极小无人机。在ARD-MAV数据集上的实验表明，该方法在挑战性条件下均表现出色，各项指标优于现有先进方法。
原文摘要 · Abstract (English)
In recent years, there has been a growing interest in the visual detection of micro aerial vehicles (MAVs) due to its importance in numerous applications. However, the existing methods based on either appearance or motion features encounter difficulties when the background is complex or the MAV is too small. In this paper, we propose a novel motion-guided MAV detector that can accurately identify small MAVs in complex and non-planar scenes. This detector first exploits a motion feature enhancement module to capture the motion features of small MAVs. Then it uses multi-object tracking and trajectory filtering to eliminate false positives caused by motion parallax. Finally, an appearance-based classifier and an appearance-based detector that operates on the cropped regions are used to achieve precise detection results. Our proposed method can effectively and efficiently detect extremely small MAVs from dynamic and complex backgrounds because it aggregates pixel-level motion features and eliminates false positives based on the motion and appearance features of MAVs. Experiments on the ARD-MAV dataset demonstrate that the proposed method could achieve high performance in small MAV detection under challenging conditions and outperform other state-of-the-art methods across various metrics
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。