We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled micro organisms in complex biological flows.
@article{arxiv.2103.01572,
title = {A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions},
author = {Mihir Durve and Fabio Bonaccorso and Andrea Montessori and Marco Lauricella and Adriano Tiribocchi and Sauro Succi},
journal= {arXiv preprint arXiv:2103.01572},
year = {2021}
}