A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

Raw sonar images may not be used for underwater detection or recognition directly because disturbances such as the grating-lobe and multi-path disturbance affect the gray-level distribution of sonar images and cause phantom echoes. To search for a more robust segmentation method with a reasonable computational cost, a prior-knowledge-based threshold segmentation method of underwater linear object detection is discussed. The possibility of guiding the segmentation threshold evolution of forward-looking sonar images using prior knowledge is verified by experiment. During the threshold evolution, the collinear relation of two lines that correspond to double peaks in the voting space of the edged image is used as the criterion of termination. The interaction is reflected in the sense that the Hough transform contributes to the basis of the collinear relation of lines, while the binary image generated from the current threshold provides the resource of the Hough transform. The experimental results show that the proposed method could maintain a good tradeoff between the segmentation quality and the computational time in comparison with conventional segmentation methods. The proposed method redounds to a further process for unsupervised underwater visual understanding.