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浙江大学学报(工学版)  2019, Vol. 53 Issue (11): 2118-2128    DOI: 10.3785/j.issn.1008-973X.2019.11.009
计算机技术与控制工程     
基于条件边界平衡生成对抗网络的河流表面流速估测
王万良1(),杨胜兰1,赵燕伟2,李卓蓉1
1. 浙江工业大学 计算机科学与技术学院,浙江 杭州 310023
2. 浙江工业大学 机械工程学院,浙江 杭州 310023
Estimation of river surface flow velocity based on conditional boundary equilibrium generative adversarial network
Wan-liang WANG1(),Sheng-lan YANG1,Yan-wei ZHAO2,Zhuo-rong LI1
1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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摘要:

针对不同流速类类间差异小而造成的分类困难问题,提出条件边界平衡生成对抗网络和多特征融合的卷积分类网络,分别进行流速图像的生成和分类. 为了达到数据增强效果,引入标签机制和验证模块实现相应类别图像数据的拟合与生成;为了加强图像不同纹理特征信息对流速估测的影响,引入多特征融合机制对所有真实样本和生成伪样本进行特征提取和流速识别,实现对差异性较小的图像的分类. 将该方法应用于实际的河流表面流速估测,结果表明,在图像生成模块中,引入的标签信息和验证机制在一定程度上能强制引导模型的数据生成方向;在图像识别模块中,引入的多特征融合机制使所提出方法相较于其他方法,在差异性较小的水流图像的识别上更具鲁棒性.

关键词: 流速估测生成式对抗网络特征融合流速类别图像验证    
Abstract:

Aiming at the difficulty in image classification due to the high similarity between different flow speeds, a conditional boundary equilibrium generative adversarial network and a convolutional classification network based on the multi-feature fusion were proposed to realize the generation and the classification of flow velocity images, respectively. A labeling mechanism and a verification module were introduced to realize the fitting and generation of corresponding category images, in order to achieve data enhancement. To enhance the impact of different texture features on velocity estimation, a multi-feature fusion layer was introduced to realize the feature extraction and the flow velocity recognition so as to realize the classification for images with small differences. The proposed method was applied to the actual river surface velocity estimation. Results demonstrate that the added tag information and the verification module can guide the data generation of corresponding class to a certain extent in the image generation module. Compared with other methods, the multi-feature fusion mechanism makes the proposed classifier more robust in identifying flow velocity images with small differences.

Key words: flow rate estimation    generative adversarial network    feature fusion    velocity category    image verification
收稿日期: 2018-09-20 出版日期: 2019-11-21
CLC:  TP 391  
基金资助: 国家科技支撑计划课题资助项目(2012BAD10B01);国家自然科学基金资助项目(61873240, 61572438)
作者简介: 王万良(1957—),男,教授,博导,从事大数据与深度学习研究. orcid.org/0000-0002-1552-5075. E-mail: wwl@zjut.edu.cn
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引用本文:

王万良,杨胜兰,赵燕伟,李卓蓉. 基于条件边界平衡生成对抗网络的河流表面流速估测[J]. 浙江大学学报(工学版), 2019, 53(11): 2118-2128.

Wan-liang WANG,Sheng-lan YANG,Yan-wei ZHAO,Zhuo-rong LI. Estimation of river surface flow velocity based on conditional boundary equilibrium generative adversarial network. Journal of ZheJiang University (Engineering Science), 2019, 53(11): 2118-2128.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.11.009        http://www.zjujournals.com/eng/CN/Y2019/V53/I11/2118

图 1  生成式对抗网络的结构框图
图 2  生成器结构示意图
图 3  判别器的结构示意图
图 4  分类网络结构示意图
图 5  基于CBEGAN的水流流速估测方法框架图
图 6  流速监测实施方案示意图
图 7  经预处理的水流图片
图 8  未经直方图均衡化的水流生成图片
图 9  经直方图均衡化后的水流生成图片
图 10  水流生成样本的准确率及类别标签损失函数分析
图 11  所提模型与VGG-16的性能比较
Va/(m?s?1 W 流速估测结果
A:V=0~0.5 m/s B:V=0.5~1.0 m/s C:V=1.0~1.5 m/s D:V=1.5~2.0 m/s E:V=2.0~2.5 m/s
0.3 3.672 1.477 ?0.097 ?6.042 ?6.034 A
0.8 ?3.348 8.604 ?1.979 ?5.569 ?12.128 B
1.2 ?3.839 3.303 15.051 0.236 ?1.108 C
1.8 ?14.221 ?7.087 ?5.498 3.204 0.674 D
2.3 ?21.496 ?15.273 6.009 10.042 13.874 E
表 1  不同流速量级下的流速估测结果
识别方法 P/%
V0=0.2 m/s V0=0.5 m/s V0=0.8 m/s
BP神经网络 63.91 71.33 72.11
SVM 67.63 73.34 76.71
SRC(40维) 52.82 54.17 55.32
RRC(40维) 63.36 65.33 65.97
VGG-16 79.68 81.27 84.12
基于CBEGAN的流速识别算法 86.79 90.38 91.67
表 2  不同流速分辨率下的多种识别方法的准确率对比
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