A robust UAV landing site detection system using mid-level discriminative patches

The forced landing problem has become one of the main impediments to UAV's entering civilian airspace. Unfortunately there is no robust forced landing site detection system that will reliably detect a safe landing site. One of the main reasons for this is the difficulty in considering the various classes of surface, to determine whether they are safe or not. We propose a robust UAV landing site detection system using mid-level discriminative patches. The training and tuning process uses a dataset containing 1600 randomly selected Google map images with weak labels. We then show how the output from multiple mid-level discriminative patch detectors can be combined to indicate the level or danger for a given region. The proposed technique reliably detects safe landing areas in UAV imagery, and achieves improved performance over the state-of-the art. The proposed system outperforms the baseline system by 29.4% for completeness and 33.9% for correctness, and is invariant to the changes of illumination, sharpness and resolution of images