Horizon detection in foggy aerial image

Abstract

Vision-based automatically landing is important for micro Unmanned Aerial Vehicles (UAVs). Horizon is a very useful clue. Most of the existing solutions for the problem can get accurate results in clear weather. However, for some images shoot in extreme environmental conditions like foggy or cloudy sky these methods are difficult in identifying the horizon correctly. In this paper, we propose a robust, vision-based horizon detection algorithm fit for this condition. The algorithm we put forward is based on a dark channel prior, which describes the depth of haze naturally. The horizon can be easily determined in dark channel property space. We then verify our vision-based horizon detection algorithm with real flying data. The results indicate that the algorithm is robust to heavy foggy weather conditions. This algorithm can also be useful in synthetic vision system.