基于水下运动目标亮点图像模型的数据增强  

Data augmentation based on highlight image models of underwater maneuvering target

作　　者：刘晓春 杨云川 胡友峰 杨向锋 李永胜 肖霖 LIU Xiaochun;YANG Yunchuan;HU Youfeng;YANG Xiangfeng;LI Yongsheng;XIAO Lin(The 705 Research Institute,China State Shipbuilding Corporation Limited,Xi'an 710077,China;Kunming Branch of the 705 Research Institute,China State Shipbuilding Corporation Limited,Kunming 650102,China)

机构地区：[1]中国船舶集团公司第705研究所,陕西西安710077 [2]中国船舶集团公司第705研究所昆明分部,云南昆明650102

出　　处：《西北工业大学学报》2024年第3期417-425,共9页Journal of Northwestern Polytechnical University

摘　　要：随着水声对抗技术的发展,深度学习技术被应用于水下目标的回波几何特征识别,但面临着样本稀缺问题。改进水下目标亮点模型,建立主动声呐目标回波信息方程,结合二者并进行空间位置的有规律变化,构成水下运动目标的亮点图像模型。以水下航行体为例详细介绍了模型的构建过程,并建立4种典型尺度诱饵的亮点图像模型实例,生成5种目标的多空间状态数据样本。设计eHasNet-5卷积分类网络,利用生成数据进行网络训练、验证和测试。试验实测数据测试表明,目标亮点图像生成模型为深度学习在主动声呐目标识别领域的应用提供了一种新的数据增强方法,生成数据训练的网络具备二维尺度目标分类能力。With the development of underwater acoustic countermeasure technology,deep learning is applied to recognize echo geometry features of underwater targets,but it faces the problem of sample scarcity.In this paper,we improved the underwater target highlight model,and established the target echo information equation of active sonar.By changing the spatial positions of target and sonar regularly,we performed the highlight image models of underwater maneuvering targets.Taking an underwater vehicle as an example,the model construction process was introduced in detail,and highlight image models of four typical acoustic scale decoys were also established,and five multi-space state highlight image data samples were generated.The eHasNet-5 convolutional classification network was designed,and the network was trained,verified and tested with the generated data.Finally,the experimental data test shows that the target highlight image generation models provide a new data augmentation method for the application of deep learning in active sonar target recognition,and the trained network by generated data has the ability to classify two-dimensional objects.

关 键 词：亮点图像 数据增强 目标分类 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TB566[自动化与计算机技术—计算机科学与技术]