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DropTrack -- automatic droplet tracking using deep learning for microfluidic applications. (arXiv:2205.02568v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
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) …