DroneDAR: 使用单目视觉和边界框特征的长距离无人机距离估计
DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
AI总结：
针对长距离小无人机距离估计的挑战，提出DroneDAR模型，结合卷积骨干网络和轻量级门控机制融合边界框特征，分析骨干容量、裁剪分辨率和回归损失对性能的影响，并探讨远距离失效模式。
摘要：
在长距离图像中准确估计小型无人机的距离对于跟踪和态势感知至关重要，但由于极端的目标尺度变化、背景杂波和噪声视觉线索，这仍然具有挑战性。本文研究了使用图像裁剪和边界框几何进行单目无人机距离估计，这是一种实际设置，其中检测器提供候选无人机区域，模型从外观和框派生特征预测距离。我们评估了一个Droneranger风格的基线，并引入了一个新的DroneDAR（无人机检测与测距）模型，该模型通过轻量级门控机制将卷积骨干网络与显式边界框线索相结合。实验分析了骨干网络容量、裁剪分辨率和回归损失函数如何影响不同距离范围内的性能。我们进一步研究了远距离下的常见失效模式，包括对边界框噪声的敏感性和裁剪中纹理细节的减少。结果为设计和训练在真实远距离条件下保持鲁棒性的距离估计器提供了指导，并指出了在无人机仅占据几个像素时提高可靠性的方向。
英文摘要：
Accurate distance estimation for small drones in long-range imagery is important for tracking and situational awareness, yet remains challenging due to extreme target scale variation, background clutter, and noisy visual cues. This paper studies monocular drone distance estimation using image crops together with bounding-box geometry, a practical setting in which a detector provides a candidate drone region and the model predicts range from appearance and box-derived features. We evaluate a Droneranger-style baseline, and introduce a new DroneDAR (Drone Detection And Ranging) model that combines a convolutional backbone with explicit bounding-box cues through a lightweight gating mechanism. Experiments analyze how backbone capacity, crop resolution, and regression loss functions affect performance across distance regimes. We further examine common failure modes at long distances, including sensitivity to bounding-box noise and reduced texture detail in the crop. The results provide guidance for designing and training range estimators that remain robust under real-world long-range conditions and highlight directions for improving reliability when drones occupy only a few pixels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
