A mathematical morphology-based detector for underwater target detection using side-scan sonar images

Underwater target detection holds significant importance in domains such as marine exploration, underwater resource management, and marine scientific research. However, due to the complexity of aquatic environments and the presence of noise interference, achieving accurate detection and recognition of underwater targets has remained a challenging task. This paper presents an algorithm grounded in mathematical morphology theory for preprocessing and feature extraction of side-scan sonar images, aimed at underwater target detection and recognition. The algorithm employs operations such as erosion, dilation, opening, and closing, which are fundamental in morphological operations. These operations enhance target outlines, edges, and spatial connectivity within the images, thereby highlighting the shape characteristics of underwater targets. Subsequently, appropriate thresholding and connected component analysis are utilized to accomplish target segmentation and localization. To validate the efficacy of the proposed algorithm, experiments were conducted on a public dataset of side-scan sonar images. Experimental results demonstrate the feasibility of the introduced mathematical morphology-based algorithm in underwater side-scan sonar target detection. In comparison with methods based on deep learning, the proposed algorithm exhibits comparable detection performance with a notable efficiency advantage under constrained computational resources.