Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications

@article{Durve2023BenchmarkingYA,
  title={Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications},
  author={Mihir Durve and Sibilla Orsini and Adriano Tiribocchi and Andrea Montessori and Jean-Michel Tucny and Marco Lauricella and Andrea Camposeo and Dario Pisignano and Sauro Succi},
  journal={ArXiv},
  year={2023},
  volume={abs/2301.08189}
}
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications Mihir Durve, a) Sibilla Orsini, 3 Adriano Tiribocchi, Andrea Montessori, Jean-Michel Tucny, 4 Marco Lauricella, Andrea Camposeo, Dario Pisignano, 5 and Sauro Succi 6 Center for Life Nano& Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161 Rome, Italy NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, 56127, Italy Istituto per… 
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References

SHOWING 1-10 OF 26 REFERENCES

Tracking droplets in soft granular flows with deep learning techniques

The procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems and the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects.

Using machine learning to discover shape descriptors for predicting emulsion stability in a microfluidic channel.

A machine learning method that learns to discover a low-dimensional code to describe droplet shapes within a concentrated emulsion, and predict whether the drop will become unstable and undergo break-up, and it is observed that 4 out of the 8 dimensions of the code are interpretable, corresponding to drop skewness, elongation, throat size, and surface curvature, respectively.

Stochastic Jetting and Dripping in Confined Soft Granular Flows.

We report new dynamical modes in confined soft granular flows, such as stochastic jetting and dripping, with no counterpart in continuum viscous fluids. The new modes emerge as a result of the…

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS…

Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation

ABSTRACT In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplet dimensionless size in a microfluidic system in order to…

Deformation and breakup dynamics of droplets within a tapered channel

In this paper we numerically investigate the breakup dynamics of droplets in an emulsion flowing in a tapered microchannel with a narrow constriction. The mesoscale approach for multicomponent fluids…

YOLOv3: An Incremental Improvement

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more…

Object Detection in 20 Years: A Survey

This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century’s time (from the 1990s to 2022).

Modeling pattern formation in soft flowing crystals

We present a mesoscale representation of near-contact interactions between colliding droplets which permits to reach up to the scale of full microfluidic devices, where such droplets are produced.…

Object Tracking Methods:A Review

A comprehensive classification is presented that classified tracking methods into four main categories of feature-based, segmentation- based, estimation-based and learning-based methods that each of which has its own sub-categories.