Adaptive Narrowband Level Set Model of Underwater Objects Detection

According to the nonlinear characteristics of original sonar image with big data, adaptive narrowband level set model to detect sonar image is proposed in this paper. In order to estimate the approximate position of the underwater objects after smoothing the original image, initial segmentation is processed with the block mode k-means clustering algorithm. On this basis, zero level set function is adaptively initialized using the approximate position of the underwater objects, which can not only reduce human intervention but also improve the detection speed. To further improve the robustness and detection accuracy, new adaptive narrowband level set model are provided to complete local optimization by minimizing each new energy function. Detection experiments demonstrate that the proposed method improves the detection speed and accuracy, and it has certain adaptability.