A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions

TL;DR

This paper introduces a deep learning method for accurately detecting and tracking droplets in dense microfluidic emulsions, outperforming traditional clustering algorithms especially with deformed droplets.

Contribution

It presents a novel deep learning-based approach that improves speed and accuracy in droplet tracking within complex microfluidic environments.

Findings

Accurately predicts droplet shape and motion

Operates at competitive rates with standard algorithms

Effective even with significant droplet deformations

Abstract

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.