Original scientific paper
https://doi.org/10.32985/ijeces.11.2.1
Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images
Ivan Aleksi
orcid.org/0000-0002-6027-7736
; Josip Juraj Strossmayer University of Osijek,Faculty of Electrical Engineering, Computer Science and Information Technology
Tomislav Matić
; Josip Juraj Strossmayer University of Osijek,Faculty of Electrical Engineering, Computer Science and Information Technology
Benjamin Lehmann
; ATLAS Elektronik GmbH
Dieter Kraus
; Hochschule Bremen, University of Applied Sciences,Institute of Water-Acoustics, Sonar-Engineering and Signal-Theory
Abstract
This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentation (RASIS) algorithm. RASIS is applied on Mine-Like Objects (MLO) in sonar images, where an object is defined by highlight and shadow regions, i.e. regions of high and low pixel intensities in a side-scan sonar image. RASIS uses a modified A*-Search method, which is usually used in mobile robotics for finding the shortest path where the environment map is predefined, and the start/goal locations are known. RASIS algorithm represents the image segmentation problem as a path-finding problem. Main modification concerning the original A*-Search is in the cost function that takes pixel intensities and contour curvature in order to navigate the 2D segmentation contour. The proposed method is implemented in Matlab and tested on real MLO images. MLO image dataset consist of 70 MLO images with manta mine present, and 70 MLO images with cylinder mine present. Segmentation success rate is obtained by comparing the ground truth data given by the human technician who is detecting MLOs. Measured overall success rate (highlight and shadow regions) is 91% for manta mines and 81% for cylinder mines.
Keywords
A*-search, image segmentation, path planning, synthetic aperture sonar
Hrčak ID:
242970
URI
Publication date:
19.6.2020.
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