Automatic Detection of Underwater Small Targets using Forward-Looking Sonar Images
Automatic Detection of Underwater Small Targets using Forward-Looking Sonar Images
复制标题
利用前视声呐图像自动探测水下小目标
DOI：
10.1109/tgrs.2022.3181417
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发表时间：
2022
影响因子：
8.2
通讯作者：
Tianchen Zhou;Jikun Si;Luyao Wang;Chao Xu;Xiaoyang Yu
中科院分区：
文献类型：
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作者：
Tianchen Zhou;Jikun Si;Luyao Wang;Chao Xu;Xiaoyang Yu
前视声纳是探测水下目标必不可少的成像设备之一。然而，由于环境复杂，从声纳图像中检测目标一直是一个挑战。提出了一种基于聚类、分割和特征判别的水下目标自动检测方法。首先，联合收割机将模糊C均值聚类（FCM）和K均值聚类相结合，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）被用来从感兴趣区域中局部分割出目标边界。最后，从目标区域中提取多个特征作为特征向量，输入非线性变换器，扩大特征距离。然后利用Fisher判别式估计分类阈值，实现了水下目标的检测。实验结果表明，该方法在低虚警概率下具有较低的检测误差和较好的实时性，不逊色于目前流行的深度学习方法。
Forward-looking sonar is one of the essential imaging equipment employed in exploring underwater targets. However, it is always challenging to detect targets from sonar images considering the complex environment. This paper represents an automatic underwater target detection method using clustering, segmentation, and feature discrimination. Firstly, 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. Secondly, 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 the 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 the popular deep learning approaches at present.