Generation Method of Underwater Samples Based on a Generative Adversarial Network

Neural network technology has become an application trend in the area of target detection of undersea high-speed vehicles, but the technology requires numerous training samples to ensure the accuracy of training results. As an important method used to solve the problem of sparse training samples, generative adversarial networks (GANs) are widely used in various fields. This study improves the classic GAN model based on the characteristics of underwater samples and proposes a generation method for underwater samples based on GAN for the purpose of augmenting training samples. First, a GAN model suitable for underwater samples is constructed, and the actual sea trial data are used to train the model and optimize the parameters. Finally, the model is used to simulate the generation of samples and verify the effectiveness of the results. Simulation results show that the generated and test samples are in good agreement and that data augmentation of the test samples can be realized. This method helps solve the problem of sparse underwater data samples and provides a reference for further application of neural networks to improve the efficiency and accuracy of undersea high-speed vehicle target detection