Fog and other poor visibility conditions hamper the visibility of runway surfaces and any obstacles present on
the runway, potentially creating a situation where a pilot may not be able to safely land the aircraft. Assisting
the pilot to land the aircraft safely in such conditions is an active area of research. We are investigating a method
that combines non-linear image enhancement with classification of runway edges to detect objects on the runway.
The image is segmented into runaway and non-runway regions, and objects that are found in the runway regions
are deemed to constitute potential hazards. For runway edge classification, we make use of the long, continuous
edges in the image stream. This paper describes a new method for edge-detection that is robust to the imaging
conditions under which we are acquiring the imagery. This edge-detection method extracts edges using a locally
adaptive threshold for the detection. The proposed algorithm is evaluated qualitatively and quantitatively on
different types of images, especially acquired under poor visibility conditions. Additionally the results of our
new algorithm are compared with other, more conventional edge detectors.