Evaluation of the required optical resolution for deep learning-based long-range UAV detection

This paper evaluates the required resolution of a telescope system experimentally to enable a reliable deep learning-based long-range UAV detection. FRCNN, a state-of-the-art deep learning object detector is fine-tuned for UAV detection with a custom dataset. A test dataset has been created of a small UAV in front of a clear and complex background at distances ranging from 500m up to 2500m using a telescope with a focal length of 1325mm and an aperture of 102 mm. At each distance the resolution is measured with a modified version of the US Air Force resolution chart. The results show that a small UAV is detected with a mAP(0.5) of above 90% in front of a complex background up to a distance of 1167m given a minimum resolution of 9:3mm or 8μrad and up to 2222m in front of a clear background given a minimum resolution of 38mm or 17:1μrad.