Generating synthetic data using GANs fusion in the digital twins model for sonars

Digital twins are a technology that allows for a virtual copy of a real object or process. The main goal is to map specific features to prevent problems or monitor conditions and operations. In this work, we propose a framework for a sonar system. Data acquired by sonar are most often sent to an operator or classification network to detect objects on the seabed. However, such a classifier needs a large amount of data to be properly trained. Therefore, we propose a digital twin model that uses generative adversarial networks (GANs) with feature fusion to obtain synthetic data for further processing. The proposed GAN model is based on dual generators with combining results that are passed further. The proposed technique indicates that building an artificial intelligence module by fusing real and synthetic data is important and allows for achieving high augmentation results in sonar applications.