基于生成对抗网络的水下样本生成方法

冷继华; 李永胜; 吕林夏; 刘礼文

doi:10.11993/j.issn.2096-3920.2021.01.011

基于生成对抗网络的水下样本生成方法

doi: 10.11993/j.issn.2096-3920.2021.01.011

中国船舶集团有限公司第705研究所, 陕西西安, 710077

详细信息

作者简介:
冷继华(1993-), 男, 在读硕士, 主要研究方向为声自导系统总体技术.

中图分类号: TJ630.1 TP183
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- 文章访问数: 186
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- PDF下载量: 134
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-30
- 修回日期: 2020-10-12
- 刊出日期: 2021-03-01

Generation Method of Underwater Samples Based on a Generative Adversarial Network

The 705 Research Institute, China State Shipbuilding Corporation Limited, Xian 710077, China

摘要

摘要: 神经网络技术已成为水下高速航行器目标检测的应用趋势, 该技术需要大量的训练样本以保证训练结果的准确性。生成对抗网络(GAN)作为解决训练样本稀少问题的重要方法, 被广泛应用在各个领域。文中针对水下样本特点对经典GAN模型进行改进, 提出一种基于GAN的水下样本生成方法, 以达到扩增训练样本的目的。首先构建适用于水下样本的GAN模型, 然后以实航试验数据训练模型并优化参数, 最后用该模型进行样本生成仿真并验证生成结果的有效性。仿真结果表明, 生成样本与试验样本吻合较好, 可实现试验样本的数据增强。该方法将有助于解决水下数据样本稀少问题, 为进一步应用神经网络提高水下高速航行器目标检测的效率和准确率提供参考。
- 水下高速航行器 /
- 生成对抗网络 /
- 数据增强 /
- 水下样本 /
- 目标检测
Abstract: 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
- undersea high-speed vehicle /
- generative adversarial network(GAN) /
- data augmentation /
- underwater samples /
- target detection

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