Depth from defocus technique with convolutional neural networks for high particle concentrations

IF 2.3 3区 工程技术 Q2 ENGINEERING, MECHANICAL Experiments in Fluids Pub Date : 2024-12-14 DOI:10.1007/s00348-024-03933-7
Rixin Xu, Zuojie Huang, Wu Zhou, Cameron Tropea, Tianyi Cai
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Abstract

Recent advantages in the depth from defocus technique for the size and location determination of particles in dispersed two-phase flows have enabled the technique to detect and analyze spherical particle images in flow systems with high number concentrations. In the present study, the use of convolutional neural networks for this task will be explored, with comparisons to the conventional analyses in terms of accuracy, tolerable concentration limits and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. Thus, the first development to be presented is an image generation procedure for out-of-focus neighboring spherical particles resulting in a known blurred image overlap. This image generation procedure is validated using laboratory images of known particle size distribution, position and image overlap, before creating a teaching dataset. The trained processing scheme is then applied to both synthetic datasets and to experimental data. The synthetic datasets allow limits of image overlap and tolerable volume concentration limits of the technique to be evaluated as a function of particle size distribution.(https://github.com/xu200911/Generate-overlapping-out-of-focus-particles)

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利用卷积神经网络对高浓度颗粒进行深度离焦分析
离焦深度技术在确定分散两相流中颗粒的大小和位置方面的最新优势,使该技术能够检测和分析高浓度流动系统中的球形颗粒图像。在本研究中,将探索卷积神经网络在此任务中的使用,并在准确性,可容忍的浓度限制和计算速度方面与传统分析进行比较。这种方法需要一个庞大的教学图像数据集,只有当数据集能够综合生成时,才具有实用性和可行性。因此,要提出的第一个发展是一个图像生成程序的失焦邻近球形粒子导致一个已知的模糊图像重叠。在创建教学数据集之前,使用已知粒度分布、位置和图像重叠的实验室图像验证该图像生成程序。然后将训练好的处理方案应用于合成数据集和实验数据。合成数据集允许将该技术的图像重叠限制和可容忍的体积浓度限制作为粒径分布的函数进行评估(https://github.com/xu200911/Generate-overlapping-out-of-focus-particles)。
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来源期刊
Experiments in Fluids
Experiments in Fluids 工程技术-工程:机械
CiteScore
5.10
自引率
12.50%
发文量
157
审稿时长
3.8 months
期刊介绍: Experiments in Fluids examines the advancement, extension, and improvement of new techniques of flow measurement. The journal also publishes contributions that employ existing experimental techniques to gain an understanding of the underlying flow physics in the areas of turbulence, aerodynamics, hydrodynamics, convective heat transfer, combustion, turbomachinery, multi-phase flows, and chemical, biological and geological flows. In addition, readers will find papers that report on investigations combining experimental and analytical/numerical approaches.
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