Particle Image Velocimetry Based on a Lightweight Deep Learning Model

Changdong Yu; Xiaojun Bi; Yang Han; Haiyun Li; Yunfei Gui

doi:10.3788/AOS202040.0720001

[1] Adrian R J. Multi-point optical measurements of simultaneous vectors in unsteady flow: a review[J]. International Journal of Heat and Fluid Flow, 7, 127-145(1986).

[2] Raffel M, Willert C E, Wereley S T et al. Particle image velocimetry[M]. Berlin: Springer-Verlag Berlin Heidelberg(2007).

[3] Westerweel J. Digital particle image velocimetry: theory and application[D]. Delft: Delft University of Technology(1993).

[4] Horn B K P, Schunck B G. Determining optical flow[J]. Proceedings of SPIE, 0281, 319-331(1981).

[5] Cai S Z, Zhou S C, Xu C et al. Dense motion estimation of particle images via a convolutional neural network[J]. Experiments in Fluids, 60, 73(2019).

[6] Lucas B, Kanade T. An iterative image registration technique with an application to stereo vision. [C]∥Proceeding of the International Joint Conference on Artificial Intelligence (IJCAI), August 24-28, 1981, Vancouver, British Columbia. [S.l.: s.n.], 674-679(1981).

[7] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Neural Information Processing Systems (NIPS 2012). December 3, 2012, Lake Tahoe, Nevada. [S.l.: s.n.], 1097-1105(2012).

[8] Graves A, Mohamed A R. -03-22)[2019-10-28]. https:∥arxiv., org/abs/1303, 5778(2013).

[9] Karpathy A, Toderici G, Shetty S et al. Large-scale video classification with convolutional neural networks. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1725-1732(2014).

[10] Hermann K, Grefenstette E et al. -06-10)[2019-10-28]. https:∥arxiv., org/abs/1506, 03340(2015).

[11] Rabault J, Kolaas J, Jensen A. Performing particle image velocimetry using artificial neural networks: a proof-of-concept[J]. Measurement Science and Technology, 28, 125301(2017).

[12] Lee Y, Yang H, Yin Z P. PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry[J]. Experiments in Fluids, 58, 171(2017).

[13] Cai S Z, Xu C, Gao Q et al. Particle image velocimetry based on a deep neural network[J]. Acta Aerodynamica Sinica, 37, 455-461(2019).

[14] Hui T W, Tang X O, Loy C C. LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, U T, USA. New York: IEEE, 8981-8989(2018).

[15] Hui T W, Tang X O. -03-15)[2019-10-28]. https:∥arxiv., org/abs/1903, 07414(2019).

[16] Li Y. Deep learning based particle image velocimetry technology and its application[D]. Wuhan: Huazhong University of Science and Technology, 10-12(2018).

[18] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

[19] Carlier J. Second set of fluid mechanics image sequences-Fluid image analysis and description: FP-6-513663[R]. Mannheim: University of Meannheim(2006).