| Citation: | WANG Chao，DONG Xiucheng，GU Shifu，et al.Global velocity measurement of fluorescent oil film based on deep learning optical flow method［J］.Journal of Aerospace Power，2022，37（7）：1539‑1549. doi: 10.13224/j.cnki.jasp.20220049
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In order to solve the problems of the traditional optical flow method based on priori，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 pixel/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 tunnel 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 improved HS optical flow method，this method has certain reference value for wind tunnel engineering application.
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