Automatic Detection of Underwater Small Targets Using Forward-Looking Sonar Images

Forward-looking sonar is one of the essential imaging equipment used in exploring underwater targets. However, it is always challenging to detect targets from sonar images considering the complex environment. This article represents an automatic underwater target detection method using clustering, segmentation, and feature discrimination. First, we combine the fuzzy C-means clustering (FCM) and K-means to cluster the sonar image globally to obtain as many regions of interests (ROIs) as possible. Second, the pulse coupled neural network (PCNN) is used to locally segment the target boundary from the ROIs. Finally, multiple features are extracted from the target area as the feature vector, which is inputted into the nonlinear converter to enlarge the features’ distance. Then we use Fisher discriminant to estimate the classification threshold, which realizes underwater target detection. The experimental results show that the proposed method has low detection error and good real-time performance under low false alarm probability, which is not inferior to popular deep learning approaches at present.