Global velocity measurement of fluorescent oil film based on deep learning optical flow method

被引：0

作者：

Wang C. ^{[1
]}

Dong X. ^{[1
]}

Gu S. ^{[1
]}

Zhang Z. ^{[2
]}

Qian H. ^{[1
]}

机构：

[1] School of Electrical Engineering and Electronic Information, Xihua University, Chengdu

[2] High Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, Sichuan

来源：

Hangkong Dongli Xuebao/Journal of Aerospace Power | 2022年 / 37卷 / 07期

关键词：

deep learning; fluorescent oil film; global velocity; optical flow method; robustness;

D O I：

10.13224/j.cnki.jasp.20220049

中图分类号：

学科分类号：

摘要：

In order to solve the problems of the traditional optical flow method based on pri‑ ori，such as harsh preconditions，an optical flow method based on deep learning was proposed to measure the global velocity of fluorescent oil film. The numerical simulation experiments were used to compare the improved HS optical flow method based on prior with FlowNet2 optical flow method based on deep learning. The results showed that the average endpoint errors of improved HS optical flow method and FlowNet2 optical flow method were 0. 458 7 pixel/s and 0. 381 7 pix‑ el/s without external interference，respectively；the average endpoint error of FlowNet2 optical flow method was significantly lower than that of HS optical flow method under the conditions of brightness change， noise disturbance or different evolution times， and the maximum difference of average endpoint errors could reach 5. 19 pixel/s. The experimental results in the wind tun‑ nel further prove that the FlowNet2 optical flow method can obtain the correct， clear and quantitative global velocity fields of fluorescent oil film. With stronger robustness than the im‑ proved HS optical flow method，this method has certain reference value for wind tunnel engineer‑ ing application. © 2022 BUAA Press. All rights reserved.

引用

页码：1539 / 1549

页数：10

共 27 条

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