<title>Robust detection of sea mines in side-scan sonar imagery based on advanced gray-scale morphological filters</title>

Abstract

Computing Devices Canada, a General Dynamics company, undertakes research in image processing focusing on the automatic recognition of sea mines. This paper presents the use of advanced gray-scale morphological filters for the detection of sea mines in side-scan sonar imagery. Sea mines in side-scan sonar imagery can be characterized by a mine-body and a mine shadow. Mine-bodies consist of bright regions, relative to the background, with a specific shape and size. Mine-shadows consist of dark regions, relative to the background, with a specific shape and size. The shapes and sizes of these regions depend on the mine type, the orientation of the mine, the physical acquisition process of the sonar imagery, and the environment in which the mine is located. Advanced gray-scale morphological filters provide very powerful and robust tools to extract bright and dark regions with low signal to noise ratio in very noisy imagery using geometric constraints such as shape, size and total surface area. For the detection of sea mines we use these morphological filters with the minimum and maximum geometric constraints for the mine-bodies and mine-shadows. The independent detection of mine-bodies and mine-shadows allows the detection of bottom, moored and drifting mines with the same detection algorithm. Consistent mine-body and mine-shadow combinations are resolved into mine like objects.