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Deep neural networks are rapidly emerging as data analysis tools, oftenoutperforming the conventional techniques used in complex microfluidic systems.One fundamental analysis frequently desired in microfluidic experiments iscounting and tracking the droplets. Specifically, droplet tracking in denseemulsions 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-edgealgorithms for object detection (YOLO) and object tracking (DeepSORT) arecombined into a single image analysis tool, DropTrack, to track droplets inmicrofluidic experiments. DropTrack analyzes input videos, extracts droplets'trajectories, and infers other observables of interest, such as dropletnumbers. Training an object detector network for droplet recognition withmanually annotated images is a labor-intensive task and a persistentbottleneck. This work partly resolves this problem by training object detectornetworks (YOLOv5) with hybrid datasets containing real and synthetic images. Wepresent an analysis of a double emulsion experiment as a case study to measureDropTrack's performance. For our test case, the YOLO networks trained with 60%synthetic images show similar performance in droplet counting as with the onetrained using 100% real images, meanwhile saving the image annotation work by60%. DropTrack's performance is measured in terms of mean average precision(mAP), mean square error in counting the droplets, and inference speed. Thefastest configuration of DropTrack runs inference at about 30 frames persecond, well within the standards for real-time image analysis.

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