比较两种深度学习方法以确定乳化流动过程中的液滴大小

IF 2 4区 化学 Q3 CHEMISTRY, MULTIDISCIPLINARY Journal of Flow Chemistry Pub Date : 2024-07-30 DOI:10.1007/s41981-024-00330-3
Inga Burke, Thajeevan Dhayaparan, Ahmed S. Youssef, Katharina Schmidt, Norbert Kockmann
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引用次数: 0

摘要

为了对多相工艺进行可靠监控,液滴大小是一个关键的质量属性,需要加以监测。由于光学测量是确定液滴大小分布最常用的技术,因此使用智能图像流量传感器是一种很有前途的方法。为此,我们比较了 Mask RCNN 和 YOLOv4 这两种不同的基于人工智能的物体检测方法的准确性及其在乳化流动过程中的适用性。迭代优化步骤(包括数据多样化和训练参数的调整)使这两种模型在不同的图像质量和成分下都能获得稳健的检测性能。YOLOv4 显示出更好的检测性能和更准确的结果,因此在确定乳化流程中的液滴大小方面,它比 Mask RCNN 有更广阔的应用空间。最终的液滴检测模型 YOLOv4 使用 Hough Circle(HC)进行特征提取,在液-液流动系统(分散相含量为 1-15 vol.-%,液滴尺寸范围为 5-150 μm)的不同数据集中确定了可靠的液滴尺寸。对置信分(CS)的调整进行评估,可确保更小液滴的统计代表性。将 YOLOv4 最终模型的液滴检测性能与人工图像处理方法进行比较,以验证模型的总体性能及其准确性和可靠性。由于 YOLOv4 与 Hough Circle(HC)相结合,显示出准确、稳健的检测和尺寸确定能力,因此适用于各种液-液流动过程的在线监测和表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

For reliable supervision in multiphase processes, the droplet size represents a critical quality attribute and needs to be monitored. A promising approach is the use of smart image flow sensors since optical measurement is the most commonly used technique for droplet size distribution determination. For this, two different AI-based object detection methods, Mask RCNN and YOLOv4, are compared regarding their accuracy and their applicability to an emulsification flow process. Iterative optimization steps, including data diversification and adaption of training parameters, enable the models to achieve robust detection performance across varying image qualities and compositions. YOLOv4 shows better detection performances and more accurate results which leads to a wider application window than Mask RCNN in determining droplet sizes in emulsification processes. The final droplet detection model YOLOv4 with Hough Circle (HC) for feature extraction determines reliable droplet sizes across diverse datasets of liquid-liquid flow systems (disperse phase content 1–15 vol.-%, droplet size range 5–150 μm). Evaluating the adjustment of Confidence Scores (CS) ensures statistical representation of even smaller droplets. The droplet detection performance of the final YOLOv4 model is compared with a manual image processing method to validate the model in general as well as its accuracy and reliability. Since YOLOv4 in combination with Hough Circle (HC) shows an accurate and robust detection and size determination, it is applicable for online monitoring and characterization of various liquid-liquid flow processes.

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来源期刊
Journal of Flow Chemistry
Journal of Flow Chemistry CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
6.40
自引率
3.70%
发文量
29
审稿时长
>12 weeks
期刊介绍: The main focus of the journal is flow chemistry in inorganic, organic, analytical and process chemistry in the academic research as well as in applied research and development in the pharmaceutical, agrochemical, fine-chemical, petro- chemical, fragrance industry.
期刊最新文献
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