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.

中文翻译：

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利用前视声纳图像自动检测水下小目标


前视声纳是探索水下目标必不可少的成像设备之一。然而，考虑到复杂的环境，从声纳图像中检测目标始终具有挑战性。本文提出了一种使用聚类、分割和特征判别的自动水下目标检测方法。首先，我们结合模糊C均值聚类（FCM）和K均值对声纳图像进行全局聚类，以获得尽可能多的感兴趣区域（ROI）。其次，脉冲耦合神经网络（PCNN）用于从 ROI 中局部分割目标边界。最后，从目标区域中提取多个特征作为特征向量，输入非线性转换器以扩大特征距离。然后利用Fisher判别式估计分类阈值，实现水下目标检测。实验结果表明，该方法在低误报概率下具有较低的检测误差和良好的实时性，不逊色于目前流行的深度学习方法。