关键词: 卷积神经网络, 显微离焦, 深度位置测量, 微通道

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.

Key words: convolutional neural networks (CNN), microscopic defocus, depth measurement, microchannel