Tracking droplets in soft granular flows with deep learning techniques

TL;DR

This paper combines deep learning algorithms YOLO and DeepSORT to accurately track droplets in complex fluid dynamic simulations, enabling real-time analysis and paving the way for automatic modeling of many-body soft systems.

Contribution

It introduces a novel application of deep learning for droplet tracking in fluid flows, trained on synthetic data, with high accuracy and real-time processing capabilities.

Findings

High accuracy droplet tracking in complex flows

Real-time analysis exceeding 30 fps on standard GPUs

Potential for automatic extraction of equations of motion

Abstract

The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The YOLO network was trained to recognize the droplets with synthetically prepared data, thereby bypassing the labor-intensive data acquisition process. In both applications, the trained YOLO + DeepSORT procedure performs with high accuracy on the real data from the fluid simulations, with low error levels in the inferred trajectories of the droplets and independently computed ground truth. Moreover, using commonly used desktop GPUs, the developed application is capable of analyzing data at speeds that exceed the typical image acquisition rates of digital cameras (30 fps), opening the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects, mostly but not exclusively, biological ones. Besides its practical applications, the procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems.