# DropTrack -- automatic droplet tracking using deep learning for microfluidic applications

arXiv: https://arxiv.org/abs/2205.02568 · HTML: https://mlsanity.com/paper/2205.02568/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 16 of 16)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Physics-informed deep learning for incompressible laminar flows](https://mlsanity.com/paper/2002.10558/) (2020)
- [Effective Use of Synthetic Data for Urban Scene Semantic Segmentation](https://mlsanity.com/paper/1807.06132/) (2018)
- [End-to-end training of object class detectors for mean average precision](https://mlsanity.com/paper/1607.03476/) (2016)
- [Human vs. supervised machine learning: Who learns patterns faster?](https://mlsanity.com/paper/2012.03661/) (2020)
- [A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest](https://mlsanity.com/paper/2103.11052/) (2021)

## Built on by (most cited first, 2 of 2)

- [Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications](https://mlsanity.com/paper/2301.08189/) (2023)
- [Deep Learning Based Object Tracking in Walking Droplet and Granular Intruder Experiments](https://mlsanity.com/paper/2302.05425/) (2023)

## Abstract (arXiv)

Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging as droplets move in tightly packed configurations. Sometimes the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection (YOLO) and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in microfluidic experiments. DropTrack analyzes input videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. This work partly resolves this problem by training object detector networks (YOLOv5) with hybrid datasets containing real and synthetic images. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. For our test case, the YOLO networks trained with 60% synthetic images show similar performance in droplet counting as with the one trained using 100% real images, meanwhile saving the image annotation work by 60%. DropTrack's performance is measured in terms of mean average precision (mAP), mean square error in counting the droplets, and inference speed. The fastest configuration of DropTrack runs inference at about 30 frames per second, well within the standards for real-time image analysis.