A Real-time Double Emulsion Droplets Detection System using Hough Circle Transform and Color Detection

Zhu, Shuo; Li, Chunxu; Rogers, John H.; Gianni, Mario; Howard, Ian S.

doi:10.1109/m2vip49856.2021.9665023

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Machine learning approaches such as the Viola-Jones algorithm train to detect objects given labelled data [51]. Successful implementations of image processing [52,53,54,55,56,57], background subtraction [58,59], and more recently machine learning techniques [60] have been readily reported in agricultural and biological contexts and, to our interest, on various forms of droplets. Readers can consult [61,62,63] for a more comprehensive review of commonly applied computer vision methods.…”

Section: Deep Learning Based Object Detectionmentioning

confidence: 99%

Deep Learning Based Object Tracking in Walking Droplet and Granular Intruder Experiments

Kara

Zhang

Williams

et al. 2023

Preprint

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We present a deep-learning based tracking objects of interest in walking droplet and granular intruder experiments. In a typical walking droplet experiment, a liquid droplet, known as walker, propels itself laterally on the free surface of a vibrating bath of the same liquid. This motion is the result of the interaction between the droplets and the surface waves generated by the droplet itself after each successive bounce. A walker can exhibit a highly irregular trajectory over the course of its motion, including rapid acceleration and complex interactions with the other walkers present in the bath. In analogy with the hydrodynamic experiments, the granular matter experiments consist of a vibrating bath of very small solid particles and a larger solid called intruder. Like the fluid droplets, the intruder interacts with and travels the domain due to the waves of the bath but tends to move much slower and much less smoothly than the droplets. When multiple intruders are introduced, they also exhibit complex interactions with each other. We leverage the state-of-art object detection model YOLO and the Hungarian Algorithm to accurately extract the trajectory of a walker or intruder in real-time. Our proposed methodology is capable of tracking individual walker(s) or intruder(s) in digital images acquired from a broad spectrum of experimental settings and does not suffer from any identity-switch issues. Thus, the deep learning approach developed in this work could be used to automatize the efficient, fast and accurate extraction of observables of interests in walking droplet and granular intruder experiments. Such extraction capabilities are critically enabling for downstream tasks such as building data-driven dynamical models for the coarse-grained dynamics and interactions of the objects of interest.

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