粒子图像测速
运动估计
测速
计算机科学
人工智能
粒子跟踪测速
卷积神经网络
估计员
光流
基本事实
流体力学
流量(数学)
湍流
计算机视觉
物理
图像(数学)
数学
机械
统计
作者
Shengze Cai,Jiaming Liang,Qi Gao,Chao Xu,Runjie Wei
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2020-06-01
卷期号:69 (6): 3538-3554
被引量:110
标识
DOI:10.1109/tim.2019.2932649
摘要
Particle image velocimetry (PIV), as a common technology for analyzing the global flow motion from images, plays a significant role in experimental fluid mechanics. In this article, we investigate the deep learning-based techniques for such a fluid motion estimation problem. The aim of this novel technique is to extract 2-D velocity fields from fluid images efficiently and accurately. First, we introduce the convolutional neural network (CNN) called LiteFlowNet, which is proposed for end-to-end optical flow estimation. Enhanced configurations of LiteFlowNet are adopted for PIV estimation in order to refine the small-scale vortex structures. Furthermore, as the supervised learning strategy is considered, a data set including particle images and the ground-truth fluid motions is generated to train the parameters of the networks. A number of fluidic images, from synthetic turbulent flow to laboratory boundary layer flow, are investigated in this article. Experimental results indicate that the proposed estimator can provide accuracy approaching that of state-of-the-art methods and high efficiency toward real-time estimation.
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