Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time.

中文翻译：

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使用深度神经网络对微通道中二元聚结液滴的混合表征。

实时物体识别和分类在许多微流体应用中至关重要，尤其是在液滴微流体中。本文讨论了卷积神经网络的应用来检测流场中合并的微滴，并根据混合程度以动态方式对它们进行分类。液滴是在采用流动聚焦和错流配置的 PMMA 微流体装置中产生的。液滴二元合并的可视化是通过连接在显微镜上的 CCD 相机进行的，并记录图像序列。部署不同的实时对象定位和分类网络（例如 You Only Look Once 和 Singleshot Multibox Detector）来进行液滴检测和表征。根据捕获的图像创建用于训练这些深度神经网络进行检测和分类的自定义数据集并手动标记。合并的液滴根据混合程度分为三类：低混合、中混合和高混合。经过训练的模型针对在不同环境条件、液滴形状、液滴尺寸和二元流体组合下拍摄的图像进行了测试，这确实在预测中表现出了很高的准确度和精确度。此外，事实证明，这些方案可以有效地从记录的视频或图像中定位聚结的二元液滴，并根据混合等级对它们进行分类，而不管实时实验条件如何。