Runway hazard detection in poor visibility conditions

Abstract

More recently, research on enhancing the situational awareness of pilots, especially in poor visibility flight conditions, gains more and more interests. Since pilots may not be able to spot the runway clearly in poor visibility conditions, such as fog, smoke, haze or dim lighting conditions, aviation landing problem can occur due to the (unexpected) presence of objects on the runway. Complicated and trivial instruments, switches, bottoms, plus sudden happenings are enough for the pilots to take care of during landing approach. Therefore, an automatic hazard detection approach that combines non-linear Multi-scale Retinex (MSR) image enhancement, edge detection with basic edge pattern analysis, and image analysis is investigated. The effect of applying the enhancement method is to make the image of the runway almost independent from the poor atmospheric conditions. The following smart edge detection process extracts edge information, which can also reduce the storing space, the comparison and retrieval time, and the effect of sensor noise. After analyzing the features existing in the edge differences occurring in the runway area by digital image processing techniques, the existing potential hazard will be localized and labeled. Experimental results show that the proposed approach is effective in runway hazard detection in poor visibility conditions.