Dense motion estimation of particle images via a convolutional neural network

In this paper, we propose a supervised learning strategy for the fluid motion estimation problem (i.e., extracting the velocity fields from particle images). The purpose of this work is to design a convolutional neural network (CNN) for estimating dense motion field for particle image velocimetry (PIV), which allows to improve the computational efficiency without reducing the accuracy. First, the network model is developed based on FlowNetS, which is recently proposed for end-to-end optical flow estimation in the computer vision community. The input of the network is a particle image pair and the output is a velocity field with displacement vectors at every pixel. Second, a synthetic dataset of fluid flow images is generated to train the CNN model. To our knowledge, this is the first time a CNN has been used as a global motion estimator for particle image velocimetry. Experimental evaluations indicate that the trained CNN model can provide satisfactory results in both artificial and laboratory PIV images. The proposed estimator is also applied to the experiment of turbulent boundary layer. In addition, the computational efficiency of the CNN estimator is much superior to those of the traditional cross-correction and optical flow methods.