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基于深度学习光流法的荧光油膜全局速度测量

王超 董秀成 古世甫 张征宇 钱泓江

王超,董秀成,古世甫,等.基于深度学习光流法的荧光油膜全局速度测量[J].航空动力学报,2022,37(7):1539‑1549. doi: 10.13224/j.cnki.jasp.20220049
引用本文: 王超,董秀成,古世甫,等.基于深度学习光流法的荧光油膜全局速度测量[J].航空动力学报,2022,37(7):1539‑1549. doi: 10.13224/j.cnki.jasp.20220049
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
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

基于深度学习光流法的荧光油膜全局速度测量

doi: 10.13224/j.cnki.jasp.20220049
基金项目: 

国家自然科学基金 11872069

四川省中央引导地方科技发展专项 2021ZYD0034

详细信息
    作者简介:

    王超(1998-),男,硕士生,主要从事空气动力学、计算机视觉研究。

    通讯作者:

    董秀成(1963-),男,教授,硕士,主要从事机器视觉研究。E⁃mail:dxc136@163.com

  • 中图分类号: V19

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

  • 摘要:

    针对基于先验的传统光流法存在前提条件苛刻的问题,提出使用基于深度学习的光流法进行荧光油膜全局速度测量。采用数值仿真试验对基于先验的改进HS光流法和基于深度学习的FlowNet2光流法进行对比,结果显示:在不外加干扰时,改进HS光流法和FlowNet2光流法的平均端点误差分别为0.458 7像素/s和0.381 7像素/s;在亮度变化、噪声干扰或不同的演化时间下,FlowNet2光流法的平均端点误差均明显低于改进HS光流法,平均端点误差差值最大可达5.19像素/s;风洞试验进一步证明,FlowNet2光流法能够获得正确、清晰、定量的荧光油膜全局速度场,较改进HS光流法鲁棒性更高,对风洞工程应用具有一定的参考价值。

     

  • 图 1  三层金字塔优化示意图

    Figure 1.  Schematic diagram of three⁃layer pyramid optimization

    图 2  FlowNet原理示意图

    Figure 2.  Schematic diagram of FlowNet priciple

    图 3  FlowNet2网络结构图

    Figure 3.  Network architecture diagram of FlowNet2

    图 4  荧光油膜模拟图像

    Figure 4.  Simulation images of fluorescent oil film

    图 5  不同亮度变化下的平均端点误差曲线

    Figure 5.  Average endpoint error curve with different brightness changes

    图 9  不同演化时间下的平均端点误差曲线

    Figure 9.  Average endpoint error curve with different evolution times

    图 10  试验平台

    Figure 10.  Experimental platform

    图 11  亮度变化下的流线图

    Figure 11.  Streamlines with brightness variation

    图 12  噪声下的流线图

    Figure 12.  Streamlines with noise

    图 13  大位移下的流线图

    Figure 13.  Streamlines with large displacement

    图 14  荧光油膜展翼图像

    Figure 14.  Fluorescent oil film images of airfoil

    表  1  两种数据集特征

    Table  1.   Features of two datasets

    特征数据集名称
    ChairsThings3D
    训练集帧数22 23221 818
    测试集帧数6404 248
    场景数9642 247
    下载: 导出CSV

    表  2  不同亮度变化下的平均端点误差

    Table  2.   Average endpoint error with different brightness changes

    亮度变化/%平均端点误差/(像素/s)
    改进HS光流法FlowNet2光流法
    -10-8-6-4-20+2+4+6+8+100.557 00.555 90.557 40.556 30.559 40.458 70.579 30.620 90.670 90.752 80.840 00.495 60.477 50.459 50.452 70.447 90.381 70.450 30.452 30.458 40.461 90.464 0
    下载: 导出CSV

    表  3  不同噪声下的平均端点误差

    Table  3.   Average endpoint error with different noises

    噪声类型平均端点误差(像素/s)
    改进HS光流法FlowNet2光流法
    无噪声0.458 70.381 7
    高斯噪声0.775 40.457 9
    椒盐噪声0.859 80.460 4
    混合噪声0.855 50.456 6
    下载: 导出CSV

    表  4  不同演化时间下的平均端点误差

    Table  4.   Average endpoint error with different evolution times

    演化时间/s平均端点误差/(像素/s)
    改进HS光流法FlowNet2光流法
    0.020.040.060.080.100.120.140.160.180.200.458 60.564 40.718 61.426 12.495 63.709 54.882 76.091 17.419 08.632 70.381 70.436 00.527 70.650 50.858 41.203 31.674 12.197 92.760 33.442 7
    下载: 导出CSV
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  • 收稿日期:  2022-01-27

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