Underwater target recognition method based on multi-domain active sonar echo images

The classification and recognition of underwater target by active sonar echo remains a challenging task due to the complex ocean environment and the multiple interferers in the sea. In this paper, an underwater target recognition method is proposed based on multi-domain active sonar echo images. The active sonar echo is first preprocessed to generate images in multiple domains. Then a deep neural network is constructed which is composed of a shared network and several domain-specific attention modules. The shared network is trained on images in all domains to get the global generalized features. The domain-specific features are then further extracted from the global feature through the attention module in each domain. The co-utilization of images in all domains enlarges the data size for training and enhances the feature representation ability of the model. Experiment results demonstrate that the features extracted from the proposed method get better recognition performance than network trained on images in single domain.