Jiang Zhe, Zhao Chen, Wang Haiyan
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China.
Sensors (Basel). 2022 Mar 16;22(6):2293. doi: 10.3390/s22062293.
Underwater target classification has been an important topic driven by its general applications. Convolutional neural network (CNN) has been shown to exhibit excellent performance on classifications especially in the field of image processing. However, when applying CNN and related deep learning models to underwater target classifications, the problems, including small sample size of underwater target and low complexity requirement, impose a great challenge. In this paper, we have proposed the modified DCGAN model to augment data for targets with small sample size. The data generated from the proposed model help to improve classification performance under imbalanced category conditions. Furthermore, we have proposed the S-ResNet model to obtain good classification accuracy while significantly reducing complexity of the model, and achieve a good tradeoff between classification accuracy and model complexity. The effectiveness of proposed models is verified through measured data from sea trial and lake tests.
水下目标分类因其广泛的应用而一直是一个重要的研究课题。卷积神经网络（CNN）已被证明在分类方面表现出色，尤其是在图像处理领域。然而，将CNN及相关深度学习模型应用于水下目标分类时，诸如水下目标样本量小以及低复杂度要求等问题带来了巨大挑战。在本文中，我们提出了改进的深度卷积生成对抗网络（DCGAN）模型，以扩充小样本量目标的数据。所提模型生成的数据有助于在类别不平衡的条件下提高分类性能。此外，我们还提出了S-残差网络（S-ResNet）模型，以在显著降低模型复杂度的同时获得良好的分类精度，并在分类精度和模型复杂度之间实现良好的权衡。通过海上试验和湖泊测试的实测数据验证了所提模型的有效性。
