Deep learning feature extraction for target recognition and classification in underwater sonar images

摘要

This paper presents an automatic target recognition (ATR) approach for sonar onboard unmanned underwater vehicles (UUVs). In this approach, target features are extracted by a convolutional neural network (CNN) operating on sonar images, and then classified by a support vector machine (SMV) that is trained based on manually labeled data. The proposed approach is tested on a set of sonar images obtained by a UUV equipped with side-scan sonar. Automatic target recognition is achieved through the use of matched filters, while target classification is achieved with the trained SVM classifier based on features extracted by the CNN. The results show that deep learning feature extraction provide better performance compared to using other feature extraction techniques such as histogram of oriented gradients (HOG) and local binary pattern (LBP). By processing images autonomously, the proposed approach can be combined with onboard planning and control systems to develop autonomous UUVs able to search for underwater targets without human intervention.