DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features
For drone tracking and situational awareness applications, this work provides practical guidance on designing range estimators that remain robust under extreme scale variation and noise.
This paper tackles long-range monocular drone distance estimation using bounding-box features and image crops. The proposed DroneDAR model achieves improved robustness over baselines, with analysis showing that combining convolutional features with gated bounding-box cues reduces error by up to 15% at distances beyond 200m.
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.