用单目视觉和框特征实现远距离小型无人机测距。
DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
- 结合图像裁片与框几何信息，设计轻量门控机制融合特征。
- 在远距离下仍保持有效测距，尤其对几像素大小目标有提升。
- 适合无人机跟踪、态势感知等实际场景应用。
远距离小尺寸无人机的精准测距对跟踪与态势感知至关重要，但受目标尺度剧烈变化、背景杂乱及视觉噪声影响，仍具挑战性。本文研究基于单目视觉与边界框特征的无人机测距方法，设定为检测器提供候选区域后，模型从外观与框导出特征中预测距离。评估了类似Droneranger的基线模型，并提出新的DroneDAR模型，通过轻量门控机制将卷积主干与显式框特征融合。实验分析了主干容量、裁片分辨率及回归损失函数在不同距离区间的影响。进一步考察了长距离下的常见失败模式，如对框噪声敏感及裁片纹理细节减少的问题。结果为设计鲁棒的远距离测距模型提供指导，并指明提升仅占数像素目标可靠性的重要方向。
原文摘要 · Abstract (English)
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.
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