A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions
arXiv:2103.01572 · doi:10.1098/rsta.2020.0400
Abstract
We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled micro organisms in complex biological flows.
References in corpus (9)
- YOLOv3: An Incremental Improvement
- Machine learning and the physical sciences
- Deep autoregressive models for the efficient variational simulation of many-body quantum systems
- Big Data: the End of the Scientific Method?
- Mesoscale modeling of near-contact interactions for complex flowing interfaces
- Mesoscopic simulations at the physics-chemistry-biology interface
- Modeling pattern formation in soft flowing crystals
- Lattice Boltzmann simulations capture the multiscale physics of soft flowing crystals
- Wet to dry self-transitions in dense emulsions: from order to disorder and back