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. ududThe 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.
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