关键词: 生成对抗网络（GAN）, 图像生成, 深度神经网络, 水下目标识别

Abstract:

Accurate recognition of underwater targets is the prerequisite for the realization of underwater robot capture and fishing operations. Aim at the problem that the poor quality of underwater images, small sample size or class imbalance lead to low accuracy of target recognition, a fast recognition method based on Generative Adversarial Networks（GAN） is proposed.The underwater image generation model of deep convolutional neural network is built by using GAN theory, and the specific underwater target image is generated by the zero-sum game between the generator and discriminator. The central coordinate calculation function and boundary fusion function of the generated target are designed. The generated target and the background image are fused and then train underwater target recognition model.Experimental results show that the method proposed in this paper can significantly improve the accuracy of underwater target identification, which is of great significance for the realization of accurate capture of underwater targets, the promotion of underwater operations and the development of ocean resources.

Key words: Generative Adversarial Networks（GAN）, image generation, deep neural network, underwater target recognition