Web: http://arxiv.org/abs/2205.02568

May 6, 2022, 1:10 a.m. | Mihir Durve, Adriano Tiribocchi, Fabio Bonaccorso, Andrea Montessori, Marco Lauricella, Michal Bogdan, Jan Guzowski, Sauro Succi

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) …

applications arxiv cv deep deep learning learning tracking

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