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PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry

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Abstract

Velocity estimation (extracting the displacement vector information) from the particle image pairs is of critical importance for particle image velocimetry. This problem is mostly transformed into finding the sub-pixel peak in a correlation map. To address the original displacement extraction problem, we propose a different evaluation scheme (PIV-DCNN) with four-level regression deep convolutional neural networks. At each level, the networks are trained to predict a vector from two input image patches. The low-level network is skilled at large displacement estimation and the high- level networks are devoted to improving the accuracy. Outlier replacement and symmetric window offset operation glue the well- functioning networks in a cascaded manner. Through comparison with the standard PIV methods (one-pass cross-correlation method, three-pass window deformation), the practicability of the proposed PIV-DCNN is verified by the application to a diversity of synthetic and experimental PIV images.

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Notes

  1. https://github.com/yongleex/PIV-DCNN.

  2. http://www.vlfeat.org/matconvnet/.

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Acknowledgements

We would like to thank all the professional editor and reviewers for the substantial effort and expertise that contribute to this work. This work was supported by National Natural Science Foundation of China (Grant Nos. 51327801 and 51475193) and the Major Project Foundation of Hubei Province (Grant No. 2016AAA009).

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yong Lee, Hua Yang & Zhouping Yin

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  1. Yong Lee
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  2. Hua Yang
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  3. Zhouping Yin
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Correspondence to Hua Yang.

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Lee, Y., Yang, H. & Yin, Z. PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry. Exp Fluids 58, 171 (2017). https://doi.org/10.1007/s00348-017-2456-1

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