Abstract
Particle Image Velocimetry (PIV) is a classical flow estimation problem which is widely considered and utilised, especially as a diagnostic tool in experimental fluid dynamics and the remote sensing of environmental flows. Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem. These supervised learning based methods are driven by large volumes of data with ground truth training information. However, it is difficult to collect reliable ground truth data in large-scale, real-world scenarios. Although synthetic datasets can be used as alternatives, the gap between the training set-ups and real-world scenarios limits applicability. We present here what we believe to be the first work which takes an unsupervised learning based approach to tackle PIV problems. The proposed approach is inspired by classic optical flow methods. Instead of using ground truth data, we make use of photometric loss between two consecutive image frames, consistency loss in bidirectional flow estimates and spatial smoothness loss to construct the total unsupervised loss function. The approach shows significant potential and advantages for fluid flow estimation. Results presented here demonstrate that our method outputs competitive results compared with classical PIV methods as well as supervised learning based methods for a broad PIV dataset, and even outperforms these existing approaches in some difficult flow cases. Codes and trained models are available at https://github.com/erizmr/UnLiteFlowNet-PIV.
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References
Adrian, R., Westerweed, J.: Particle Image Velocimetry. Cambridge University Press, Cambridge (2011)
Hinton, G., Sejnowski, T.: Unsupervised Learning: Foundations of Neural Computation. MIT Press, Cambridge (1999)
Westerweel, J.: Fundamentals of digital particle image velocimetry. Exp. Fluids 23(12), 1379–1392 (1997)
Horn, B., Schunck, B.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)
Ruhnau, P., Kohlberger, T., Schnorr, C., Nobach, H.: Variational optical flow estimation for particle image velocimetry. Exp. Fluids 38(1), 21–32 (2005)
Rabault, J., Kolaas, J., Jensen, A.: Performing particle image velocimetry using artificial neural networks: a proof-of-concept. Meas. Sci. Technol. 28(12), 125301 (2017)
Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2 (2017)
Hui, T., Tang, X., Loy, C.: LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8981–8989 (2018)
Lee, Y., Yang, H., Yin, Z.: PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry. Exp. Fluids 58(12), 1–10 (2017). https://doi.org/10.1007/s00348-017-2456-1
Cai, S., Zhou, S., Xu, C., Gao, Q.: Dense motion estimation of particle images via a convolutional neural network. Exp. Fluids 60(4), 1–16 (2019). https://doi.org/10.1007/s00348-019-2717-2
Cai, S., Liang, J., Gao, Q., Xu, C., Wei, R.: Particle image velocimetry based on a deep learning motion estimator. IEEE Trans. Instrum. Meas. 69(6), 3538–3554 (2019)
Yu, J.J., Harley, A.W., Derpanis, K.G.: Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 3–10. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_1
Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical flow with a bidirectional census loss. In: The Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Sun, D., Roth, S., Black, M.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115–137 (2014)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: NIPS’15: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 2, pp. 2017–2025 (2015)
Zhang, C., Li, Z., Cai, R., Chao, H., Rui, Y.: As-rigid-as-possible stereo under second order smoothness priors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 112–126. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_8
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)
Li, Y., et al.: A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. J. Turbul. 9(9), N31 (2008)
Acknowledgements
The authors would like to acknowledge funding from the Chinese Scholarship Council and Imperial College London (a pump priming research award from the Energy Futures Lab, Data Science Institute and Gratham Institute – Climate Change and the Environment) that supported this work.
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Zhang, M., Piggott, M.D. (2020). Unsupervised Learning of Particle Image Velocimetry. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12321. Springer, Cham. https://doi.org/10.1007/978-3-030-59851-8_7
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