In the last decade, deep learning architectures have overwhelmingly outperformed the state-of-the-art in many traditional computer vision tasks as image classification and object detection, so that it becomes interesting to test them on new tasks such as regression. Particle image velocimetry (PIV) is an experimental fluid dynamics technique that involves image analysis. PIV processing represents a complex regression problem. Therefore, it is interesting to test the ability of artificial neural networks (ANNs) to perform PIV. The first feasibility study on ANN application to PIV was "Performing particle image velocimetry using artificial neural networks: a proof-of-concept" published in 2017 by Rabault et al.