An underwater small target boundary segmentation method in forward-looking sonar images

Abstract

Underwater target boundary segmentation is integral to forward-looking sonar image processing. However, small target boundary segmentation is always challenging due to the low resolution, low signal-to-noise ratio, and inhomogeneity of intensity of forward-looking sonar images. This paper proposes an improved level set segmentation method to accurately obtain the contour of small targets in the forward-looking sonar images: morphological reconstruction combined with the level set method (MRLSM). Compared with the classical level set and the level set method combined with the morphological method, MRLSM improves the accuracy and stability for forward-looking sonar image segmentation. Furthermore, the fuzzy C-means, Markov random field, and MRLSM are applied to the numerical simulations and experimental data for comparison. The deep learning methods are also used to compare the performances. The results demonstrated that the proposed method is more accurate, robust, and considerable for underwater small target boundary segmentation.