Sonar digital twin layer via multi-attention networks with feature transfer

Analysis of the seabed using sonar is a key technology enabling the assessment of the substrate, detection and classification of objects located there. However, quite often sonar data is processed by users due to the small amount of measurement data. This is due to the need to create large data sets, and creating a sonar image is often dependent on atmospheric conditions. In this paper, we present a solution based on digital twins that allows the implementation of a digital twin layer for sonar applications. A digital twin layer based on generative and classification network models increases the amount of data and improves the effectiveness of solutions. For this purpose, we propose multi-attention models that focus on local and global sonar features and enable their fusion. Moreover, a technique for exchanging weights between networks in such a solution was modeled to reduce the amount of computing power. The proposed approach allows for analyzing images by focusing on different features and increasing the automatization of processing its data. To verify the operation, various sonar data were used and high classification accuracy was achieved as well as the generation of new data.