Modified whale optimization algorithm for underwater image matching in a UUV vision system

Abstract

In this paper, a hybrid whale optimization algorithm based on the Levy flight strategy (LWOA) and lateral inhibition (LI) is proposed to solve the underwater image matching problem in an unmanned underwater vehicle (UUV) vision system. The proposed image matching technique is called the LI-LWOA. The whale optimization algorithm (WOA) simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. The algorithm not only can balance the exploration and exploitation but also has high calculation accuracy. The Levy flight strategy can expand the search space to avoid premature convergence and enhance the global search ability. In addition, the lateral inhibition mechanism is applied to conduct image preprocessing, which enhances the intensity gradient and image characters, and improves the image matching accuracy. The LI-LWOA achieves the complementary advantages of the LWOA and lateral inhibition to improve the image matching accuracy and enhance the robustness. To verify the overall optimization performance of the LI-LWOA, a series of underwater image matching experiments that seek to maximize the fitness value are performed, and the matching results are compared with those of other algorithms. The experimental results show that the LI-LWOA has better fitness, higher matching accuracy and stronger robustness. In addition, the proposed algorithm is a more effective and feasible method for solving the underwater image matching problem.