A research on underwater target recognition neural network for small samples

In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering. However, in the face of objective problems such as the lack of underwater target samples, the complex underwater sound environment, and the poor sample signal-to-noise ratio, the deep learning also becomes less sensitive due to its own limitations. In this paper, by constructing a variety of target feature extraction methods and a deep neural network model, we obtain the target recognition rate network prediction value after matched different target feature extraction with neural network model. Through comparing experimental results, a new idea of solving small sample target identification through deep neural network deep design is proposed