LightPIVNet: An Effective Convolutional Neural Network for Particle Image Velocimetry

Particle image velocimetry (PIV) plays a significant role in experimental fluid mechanics, which aims to extract the velocity fields from successive particle image pairs. Deep learning (DL) techniques have been proposed to solve such a fluid motion estimation problem. However, the existing DL methods put emphasis on accuracy while ignoring the problem of model redundancy and low computational efficiency. In this article, we propose a novel lightweight convolutional neural network called LightPIVNet for PIV estimation, which is targeted to improve the advanced optical flow model Recurrent All-Pairs Field Transforms (RAFT). Furthermore, considering the real fluid scene, we generated a new dataset PIV-Dataset-II to train our network, which increases the amount and variety of flow fields. Our approach has been verified and analyzed on synthetic and experimental particle images. The experimental results indicate that our method achieves state-of-the-art performance among all the DL methods and is much superior to those classical traditional methods, such as WIDIM and HS optical flow. Meanwhile, our model LightPIVNet reduces parameters by 40.2% and improves inference time by 14.3% compared to the current best DL model PIV-LiteFlowNet-en.