| 基于深度学习的显微离焦图像法颗粒深度测量 |
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| 引用本文： | 徐日辛,周骛,张翔云.基于深度学习的显微离焦图像法颗粒深度测量[J].化工进展,2021,40(12):6499-6504. |
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| 作者姓名： | 徐日辛 周骛 张翔云 |
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| 作者单位： | 上海理工大学能源与动力工程学院,上海200093;上海市动力工程多相流动与传热重点实验室,上海200093 |
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| 基金项目： | 国家科技重大专项(2017-V-0016-0069);国家自然科学基金(51576130) |
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| 摘 要： | 显微成像条件下的三维流场测量是微通道流动等研究的基础，其难点在于颗粒深度位置的测量。由于显微镜头景深极小，成像时通道内大部分颗粒处于离焦状态。本文首先基于几何光学原理分析了显微成像前后离焦 不对称的特点，随后基于Inception V3卷积神经网络搭建了颗粒深度预测模型，并通过光线追踪方法生成粒径 1~10μm的10种颗粒在深度范围-50～50μm内的仿真显微图像，应用深度预测模型对其进行训练及预测，颗粒深度预测结果显示1～3μm颗粒的相对误差在±13%以内，4～10μm颗粒的相对误差小于±5%。最后在微通道中拍摄粒径分别为2.6μm和5μm的聚苯乙烯微球在深度范围-50～50μm内的显微图像，应用同一深度预测模型进行训练及预测，两种颗粒深度预测结果的相对误差分别为小于±15%和±5%。结果表明，所提出的基于深度学习的显微离焦图像法能够有效测量微通道内颗粒深度位置，为图像法流场测量技术增加了新的思路。
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| 关 键 词： | 卷积神经网络 显微离焦 深度位置测量 微通道 |
| 收稿时间： | 2021-07-01 |
Particle depth position measurement using microscopic defocused imaging method based on deep learning |
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| XU Rixin,ZHOU Wu,ZHANG Xiangyun.Particle depth position measurement using microscopic defocused imaging method based on deep learning[J].Chemical Industry and Engineering Progress,2021,40(12):6499-6504. |
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| Authors: | XU Rixin ZHOU Wu ZHANG Xiangyun |
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| Affiliation: | 1.School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai 200093, China |
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| Abstract: | Three-dimensional flow field measurement using microscopic imaging is the foundation of research in microchannel flow, and one difficulty lies in the measurement of particle depth position. Due to the limited depth of field of the microscope, most particles in the microchannel will be out of focus during imaging. In this paper, based on the principle of geometrical optics, the characteristics of asymmetric defocus was analyzed for microscopic imaging. A particle depth prediction model was built based on the Inception V3 convolutional neural network, and the simulated microscopic images of ten sizes of particles with diameters 1—10μm in the depth range of -50—50μm were generated by the optical ray tracing method. The particle depth prediction model was trained and used for prediction with these synthetic images. The results showed that the relative error of prediction for 1—3μm particles was within ±13%, and less than ±5% for 4—10μm particles. Finally, the microscopic images of polystyrene microspheres with sizes of 2.6μm and 5μm in the depth range of -50—50μm were captured in the microchannel, and the same depth prediction model was used for training and prediction. The relative errors of depth prediction for the two sizes of particles were less than ±15% and ±5%, respectively. The microscopic defocused imaging method based on deep learning can measure the depth position of particles in microchannel effectively, adding new ideas to the imaging method for flow field measurement technology. |
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| Keywords: | convolutional neural networks (CNN) microscopic defocus depth measurement microchannel |
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