Applicability of deep learning optical flow estimation for PIV methods

Abstract

Recently, there have been some attempts to introduce the method of deep learning-based optical flow(DL-based OF) into PIV measurements. These attempts have made it possible to estimate the flow field with higher accuracy and higher spatial resolution. The current mainstream deep-learning optical flow estimation method often requires a training set whose data distribution is closer to the real sample when completing the target task. However, there is a certain difference between the rigid body motion dataset commonly used in the field of computer vision and the fluid motion dataset in PIV measurements. Whether networks trained with rigid-body data can be directly applied to fluids has not been fully discussed. However, in existing attempts to introduce the DL-based OF method into the field of PIV measurement, the influence of the characteristics of the PIV particle image on the calculation results of the deep learning optical flow estimation (DL-based OF) has not been fully discussed. Therefore, the boundary conditions of the learning method for PIV calculation cannot be fully demonstrated. In this study, we selected the Flownet2 network, which is a classical baseline model on rigid body datasets, as a representative deep learning optical flow estimation method for experiments. Based on the characteristics of various particle images, this study generates a dataset with a wide range of parameters, such as particle density, particle diameter, sheet thickness, and maximum displacement, which are key to the generation of PIV simulation images and discusses the sensitivity of DL-based OF methods under extreme parameter conditions. This study compares (1) the model parameters trained using rigid body datasets such as Sintel, (2) the model parameters trained using synthetic PIV images, (3) the PIV-WIDIM method, and (4) the Optical Flow-Farneback method. The results reveal that the DL-based OF methods in (1) and (2) have a higher spatial resolution and estimation accuracy than the traditional methods in (3)] and (4). Training with the synthetic fluid dataset results in a smoother flow field in method (2)] compared with training with the rigid body dataset in method (1). In the case of limited training set parameters, Flownet2 can still be suitable for extreme conditions, such as small particle concentration, small particle diameter, insufficient particle brightness, or small particle displacement.