Research on an underwater high-speed vehicle target recognition method based on deep learning

As the only high-speed navigation equipment in the ocean, the target recognition performance of the underwater high-speed vehicle determines the final completion effect of the mission. Due to the complexity of marine environment and the constantly upgrading of new countermeasure equipment, underwater high-speed vehicles are currently faced with the problem of insufficient recognition ability in complex marine environment, and it is urgent to find a new way of feature extraction and target recognition. Based on the good feature mining ability of deep convolutional networks and the characteristics of echo signals, a deep learning underwater target recognition model is proposed in this paper, and the model verification experiment is carried out by using test site data. At the same time, to solve the problem of insufficient training data, a generative adversarial networks is established to expand the data set. The experimental results show that the deep learning model proposed in this paper can effectively identify underwater targets, and the model recognition accuracy is improved by generating adversarial network data set expansion, which provides a new idea for the intelligent development of underwater high-speed vehicles.