Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (9): 1752-1763    DOI: 10.3785/j.issn.1008-973X.2021.09.017
土木工程、水利工程     
基于图像的河流表面测速研究综述
杨聃1(),邵广俊1,胡伟飞1,刘国富1,梁家铭2,王瀚林2,许超2
1. 国网浙江省电力有限公司 紧水滩水力发电厂，浙江 丽水 323000
2. 浙江大学 控制科学与工程学院，浙江 杭州 310058
Review of image-based river surface velocimetry research
Dan YANG1(),Guang-jun SHAO1,Wei-fei HU1,Guo-fu LIU1,Jia-ming LIANG2,Han-lin WANG2,Chao XU2
1. Jinshuitan Hydropower Plant of State Grid Zhejiang Electric Power Limited Company, Lishui 323000, China
2. College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
 全文: PDF(1506 KB)   HTML
摘要：

为了解决洪涝灾害环境下设备难布设、流速难测量、河流难监控等问题，结合河流管控领域近10年研究情况，基于非侵入式、低成本、高效的测量手段，概述从粒子图像测速技术（PIV）到深度学习方法的一系列图像测速技术. 从图像采集、图像分析、图像后处理等方面探讨河流表面测速机理及存在的问题. 通过比对与总结各方法差异性，提出对现有方法的改进需求，旨在提升河流流速的测量效率.
关键词： 图像测速;  河流流量监测;  河流表面流速;  图像分析;  大尺度粒子图像测速    
Abstract:

In order to solve the problems of difficult equipment deployment, velocity measurement and river monitoring in flooding environment, a series of image velocimetry techniques from particle image velocimetry (PIV) to deep learning methods were outlined based on non-invasive, low-cost and efficient measurement means in conjunction with nearly ten years of research in the field of river monitoring. The mechanism and issues of river surface velocimetry were discussed in the sections of image acquisition, image analysis, and image post-processing. By comparing and summarizing the differences of each method, the requirement of the existing methods were proposed, aiming to improve the river flow velocity measurement efficiency.
Key words: image velocimetry    river flow monitoring    river surface flow velocity    image analysis    large scale particle image velocimetry
收稿日期: 2020-09-16 出版日期: 2021-10-20
CLC:  TP 391  
基金资助: 国网浙江省电力有限公司科技项目（5211JS18001200K3100000）
作者简介: 杨聃（1976—），男，硕士，高级工程师，从事水利工程研究. orcid.org/0000-0001-9071-7328. E-mail: 12937401@qq.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
杨聃
邵广俊
胡伟飞
刘国富
梁家铭
王瀚林
许超

引用本文:

杨聃,邵广俊,胡伟飞,刘国富,梁家铭,王瀚林,许超. 基于图像的河流表面测速研究综述[J]. 浙江大学学报(工学版), 2021, 55(9): 1752-1763.

Dan YANG,Guang-jun SHAO,Wei-fei HU,Guo-fu LIU,Jia-ming LIANG,Han-lin WANG,Chao XU. Review of image-based river surface velocimetry research. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1752-1763.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.09.017    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I9/1752

算法 概述 优点 缺点
LSPIV[3, 6] 在前一帧的查询区域和后一帧的搜索区域间进行相关分析操作. 查询区域和搜索区域根据流动情况人为定义. 相关系数最大的2个窗口对应最可能的位移. 相关分析操作主要分为空域互相关和快速傅里叶变换互相关 可以提供全局的表面流速场. 简单有效，是目前应用较广的技术 空域互相关计算量较大. 算法得到的是每个查询区域的平均速度，速度场分辨率不高
LSPTV[7] 包括粒子检测和跟踪共2个步骤. 获取图像中粒子的空间分布情况，并进一步获取粒子的亚像素质心位置. 在跟踪阶段，根据粒子动态特性和分配算法，匹配不同时间帧中的粒子质心，连接粒子质心以重建轨迹 在稀疏粒子的情况下表现良好. 相对于粒子图像测速，算出的流场速度具有真实物理意义 无法处理粒子浓度较高的情况，对部分设定参数较敏感
SSIV[8] 在LSPIV的相关分析操作的基础上，得到初步的全局速度矢量场，再利用表面流的特性对矢量场进行后处理. 其核心在于去除LSPIV结果中的错误矢量 对查询区域变化不敏感，在复杂光学环境（如阴影或眩光）中仍有较好的
结果 		互相关分析以及对矢量场进行后处理的过程计算量较大
OTV[9] 结合自动特征检测、Lucas-Kanade跟踪算法和基于轨迹的过滤方法.利用Fast算法检测水面目标角点特征，用基于图像金字塔的Lucas-Kanade稀疏光流跟踪算法跟踪以上特征，再对跟踪轨迹进行后处理 在表面目标特征稀疏、非恒定流情况时表现良好 所得速度场分辨率不高
STIV[10] 先合成时空图像，再利用傅里叶变换检测时空图像的纹理主方向，该主方向对应原图像序列的一维时均流速矢量 空间分辨率高、实时性强，可用于河流的实时
监测 		对复杂光照和水流环境的变化较为敏感
基于概率的测速 [11] 基于概率的图像测速方法。基于贝叶斯框架，在图像观测函数已知的情况下，估计像素速度矢量场的后验概率 可以得到稠密的光流矢量场 为了提高流场精度，须改进超参数估计以及描述条件概率分布的似然估计
基于机器学习的测速[12-14] 基于有监督学习的机器学习表面流测速方法，主要进行水流表面特征的提取和流速的分类识别. 基于无监督学习的方法，利用非线性图像配准的思路计算表面速度矢量场 有效水流表面特征的
提取 		有监督学习方法需要大量数据. 无监督学习方法的研究较少，其测速精度仍需要经过更多实验的验证；该方法仅研究了海岸波浪的跟踪测速方案，其泛化性仍有待研究
SGSD[15] 在变分光流框架中加入流体物理约束. 利用标量输运方程计算光流场，引入扩散项补充小尺度流场特征 速度矢量场结果精度
较高 		流动轨迹重建中仍对异常值存在敏感性
表 1  图像速度分析算法的对比
图 1  水面模式
图 2  不同光谱带河流表面
图 3  河流图像正射校正
图 4  LSPIV计算流场结果
图 5  基于相关操作的速度矢量估计
图 6  基于FFT-CC法的流程图
图 7  WIDIM算法示意图
图 8  时空图像示意图
图 9  测速线及其时空图像
图 10  LSPTV方法流程图
图 11  4帧法的原理示意图
图 12  LSPTV下的粒子识别与所测流场
图 13  基于概率的结果估计
图 14  基于FAST的OTV滤波轨迹图像
图 15  河流表面图像与压缩感知后的特征
图 16  SSIV法滤波后的表面流场
1 LE B R, PÉNARD L, HAUET A, et al Gauging extreme floods on YouTube: application of LSPIV to home movies for the post-event determination of stream discharges[J]. Hydrological Process, 2016, 30 (1): 90- 105
doi: 10.1002/hyp.10532
2 KASUGA K, HACHIYA H, KINOSITA T, et al Quantitative estimation of the ultrasound transmission characteristics for river flow measurement during a flood[J]. Japanese Journal of Applied Physics, 2003, 42 (5): 3212- 3215
3 LE C J, HAUET A, PIERREFEU G, et al Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in mediterranean rivers[J]. Journal of Hydrology, 2010, 394 (1-2): 42- 52
doi: 10.1016/j.jhydrol.2010.05.049
4 LEWIS Q W, RHOADS B L Resolving two-dimensional flow structure in rivers using large-scale particle image velocimetry: an example from a stream confluence[J]. Water Resources Research, 2015, 51 (10): 7977- 7994
doi: 10.1002/2015WR017783
5 FUJITA I, NOTOYA Y, FURUTA T. Measurement of inundating flow from a broken embankment by using video images shoot from a media helicopter [C]// Proceedings of the International Conference on Fluvial Hydraulics. Villeurbanne: CRC Press, 2018: 06001.
6 FUJITA I, MUSTE M, KRUGER A Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications[J]. Journal of Hydraulic Research, 1998, 36 (3): 397- 414
doi: 10.1080/00221689809498626
7 DAL S S, PIZARRO A, SAMELA C, et al Exploring the optimal experimental setup for surface flow velocity measurements using PTV[J]. Environmental Monitoring and Assessment, 2018, 190 (8): 460
doi: 10.1007/s10661-018-6848-3
8 LEITÃO J P, PEÑA-HARO S, LÜTHI B, et al Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry[J]. Journal of Hydrology, 2018, 565: 791- 804
doi: 10.1016/j.jhydrol.2018.09.001
9 TAURO F, TOSI F, MATTOCCIA S, et al Optical tracking velocimetry (OTV): leveraging optical flow and trajectory-based filtering for surface streamflow observations[J]. Remote Sensing, 2018, 10 (12): 2010
doi: 10.3390/rs10122010
10 FUJITA I, WATANABLE H, TSUBAKI R Development of a non-intrusive and efficient flow monitoring technique: the space-time image velocimetry (STIV)[J]. International Journal of River Basin Management, 2007, 5 (2): 105- 114
doi: 10.1080/15715124.2007.9635310
11 BACHARIDIS K, MOIROGIORGOU K, SIBETHEROS I A, et al. River flow estimation using video data [C]// 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings. Santorini: IEEE, 2014: 173-178.
12 王万良, 邱虹, 郑建炜 基于压缩感知图像分析的河流表面流速估计方法[J]. 水力发电学报, 2018, 37 (5): 69- 79
WANG Wan-liang, QIU Hong, ZHENG Jian-wei Estimation of river surface flow velocity through image analysis based on compressed sensing[J]. Journal of Hydroelectric Engineering, 2018, 37 (5): 69- 79
doi: 10.11660/slfdxb.20180507
13 王万良, 杨胜兰, 赵燕伟, 等 基于条件边界平衡生成对抗网络的河流表面流速估测[J]. 浙江大学学报:工学版, 2019, 53 (11): 2118- 2128
WANG Wan-liang, YANG Sheng-lan, ZHAO Yan-wei, et al Estimation of river surface flow velocity based on conditional boundary equilibrium generative adversarial network[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (11): 2118- 2128
doi: 10.3785/j.issn.1008-973X.2019.11.009
14 KIM J, KIM J Estimation of water surface flow velocity in coastal video imagery by visual tracking with deep learning[J]. Journal of Coastal Research, 2020, 95 (Suppl.1): 522- 526
15 KHALID M, PÉNARD L, MÉMIN E Optical flow for image-based river velocity estimation[J]. Flow Measurement and Instrumentation, 2019, 65: 110- 121
doi: 10.1016/j.flowmeasinst.2018.11.009
16 ZHANG Z, ZHOU Y, LI Y, et al. IP camera-based LSPIV system for on-line monitoring of river flow [C]// IEEE International Conference on Electronic Measurement Instruments. Yangzhou: IEEE, 2017: 357-363.
17 BASSET A B. A treatise on hydrodynamics: with numerous examples [M]. [S. l. ]: Deighton, Bell and Company, 1888.
18 HADAD T, GURKA R Effects of particle size, concentration and surface coating on turbulent flow properties obtained using PIV/PTV[J]. Experimental Thermal and Fluid Science, 2013, 45: 203- 212
doi: 10.1016/j.expthermflusci.2012.11.006
19 EICHENDORFF P, SCHLENKHOFF A. Continuous measurement of open channel discharge using a video data logger and subsequent LSPIV analysis [C]// EGU General Assembly Conference Abstracts. [S. l.]: Geophysical Research Abstracts (GRA), 2020: 18413.
20 ZHANG Z, WANG X, FAN T, et al River surface target enhancement and background suppression for unseeded LSPIV[J]. Flow Measurement and Instrumentation, 2013, 30: 99- 111
doi: 10.1016/j.flowmeasinst.2012.12.002
21 张振, 徐枫, 沈洁, 等 基于变高单应的单目视觉平面测量方法[J]. 仪器仪表学报, 2014, 35 (8): 1860- 1868
ZHANG Zhen, XU Feng, SHEN Jie, et al Plane measurement method with monocular vision based on variable-height homography[J]. Chinese Journal of Scientific Instrument, 2014, 35 (8): 1860- 1868
22 LI W, LIAO Q, RAN Q Stereo-imaging LSPIV (SI-LSPIV) for 3D water surface reconstruction and discharge measurement in mountain river flows[J]. Journal of Hydrology, 2019, 578: 124099
doi: 10.1016/j.jhydrol.2019.124099
23 WERELEY S T, GUI L, MEINHART C D Advanced algorithms for microscale particle image velocimetry[J]. AIAA Journal, 2002, 40 (6): 1047- 1055
doi: 10.2514/2.1786
24 DOBSON D W, HOLLAND K T, CALANTONI J Fast, large-scale, particle image velocimetry-based estimations of river surface velocity[J]. Computers and Geosciences, 2014, 70: 35- 43
doi: 10.1016/j.cageo.2014.05.007
25 WEITBRECHT V, KÜHN G, JIRKA G H Large scale PIV measurements at the surface of shallow water flows[J]. Flow Measurement and Instrumentation, 2002, 13 (5-6): 237- 245
doi: 10.1016/S0955-5986(02)00059-6
26 SCARANO F, RIETHMULLER M L Iterative multigrid approach in PIV image processing with discrete window offset[J]. Experiments in Fluids, 1999, 26 (6): 513- 523
doi: 10.1007/s003480050318
27 SCARANO F Iterative image deformation methods in PIV[J]. Measurement Science and Technology, 2001, 13 (1): R1- R19
28 LIBERZON A, GURKA R, TAYLOR Z. OpenPIV [EB/OL]. [2021-08-28].https://github.com/OpenPIV.
29 LE C J, JODEAU M, HAUET A, et al. Image-based velocity and discharge measurements in field and laboratory river engineering studies using the free FUDAA-LSPIV software [C]// Proceedings of the International Conference on Fluvial Hydraulics. Lausanne: CRC Press, 2014.
30 THIELICKE W, STAMHUIS E. PIVlab–towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB [J/OL]. Journal of Open Research software, 2014, 2(1): e30[2021-08-28]. http://doi.org/10.5334/jors.bl.
31 PATALANO A, GARCÍA C M, RODRÍGUEZ A Rectification of image velocity results (RIVeR): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (PIV) and particle tracking velocimetry (PTV)[J]. Computers and Geosciences, 2017, 109: 323- 330
doi: 10.1016/j.cageo.2017.07.009
32 PERKS M T, SASSO S F D, HAUET A, et al Towards harmonisation of image velocimetry techniques for river surface velocity observations[J]. Earth System Science Data, 2020, 12 (3): 1545- 1559
doi: 10.5194/essd-12-1545-2020
33 PEARCE S, LJUBIČIĆ R, PEÑA-HARO S, et al An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using unmanned aerial systems[J]. Remote Sensing, 2020, 12 (2): 232
doi: 10.3390/rs12020232
34 FUJITA I, DEGUCHI T, DOI K, et al. Development of KU-STIV: software to measure surface velocity distribution and discharge from river surface images [C]// Proceedings of the 37th IAHR World Congress. Kuala Lumpur: [s.n.], 2017: 5284-5292.
35 王慧斌, 董伟, 张振, 等 基于时空图像频谱的时均流场重建方法[J]. 仪器仪表学报, 2015, 36 (3): 623- 631
WANG Hui-bin, DONG Wei, ZHANG Zhen, et al Time-averaged flow field reconstruction method based on spectrum of spatio-temporal image[J]. Chinese Journal of Scientific Instrument, 2015, 36 (3): 623- 631
36 张振, 周扬, 李旭睿, 等 图像法测流系统开发与应用[J]. 水利信息化, 2018, (3): 7- 13
ZHANG Zhen, ZHOU Yang, LI Xu-rui, et al Development and application of an image-based flow measurement system[J]. Water Resources Informatization, 2018, (3): 7- 13
37 张振, 王慧斌, 严锡君, 等 时空图像测速法的敏感性分析及不确定度评估[J]. 仪器仪表学报, 2017, 38 (7): 1763- 1771
ZHANG Zhen, WANG Hui-bin, YAN Xi-jun, et al Sensitivity analysis and uncertainty evaluation of space-time image velocimetry[J]. Chinese Journal of Scientific Instrument, 2017, 38 (7): 1763- 1771
doi: 10.3969/j.issn.0254-3087.2017.07.025
38 BAEK J, LEE S J A new two-frame particle tracking algorithm using match probability[J]. Experiments in Fluids, 1996, 22 (1): 23- 32
doi: 10.1007/BF01893303
39 TANG H, CHENG C, HONG C, et al An improved PTV system for large-scale physical river model[J]. Journal of Hydrodynamics, Ser. B, 2008, 20 (6): 669- 678
doi: 10.1016/S1001-6058(09)60001-9
40 CIERPKA C, LÜTKE B, KÄHLER C J Higher order multi-frame particle tracking velocimetry[J]. Experiments in Fluids, 2013, 54 (5): 1533
doi: 10.1007/s00348-013-1533-3
41 OKAMOTO K, HASSAN Y, SCHMID W New tracking algorithm for particle image velocimetry[J]. Experiments in Fluids, 1995, 19 (5): 342- 347
doi: 10.1007/BF00203419
42 ISHIKAWA M, MURAI Y, WADA A, et al A novel algorithm for particle tracking velocimetry using the velocity gradient tensor[J]. Experiments in Fluids, 2000, 29 (6): 519- 531
doi: 10.1007/s003480000120
43 CHANG, JOHNNY, EDWARDS D, et al. Statistical estimation of fluid flow fields [C]// ECCV Workshop on Statistical Methods in Video Processing. Copenhagen: Springer, 2002.
44 PYTLAK R. Conjugate gradient algorithms in nonconvex optimization [M]. [S. l. ]: Springer Science and Business Media, 2008.
45 HÉAS P, HERZET C, MÉMIN E, et al Bayesian estimation of turbulent motion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35 (6): 1343- 1356
46 HÉAS P, HERZET C, MÉMIN E Bayesian inference of models and hyperparameters for robust optical-flow estimation[J]. IEEE Transactions on Image Processing, 2011, 21 (4): 1437- 1451
47 HORN B K P, SCHUNCK B G Determining optical flow[J]. Artifical Intelligence, 1981, 17 (1-3): 185- 203
doi: 10.1016/0004-3702(81)90024-2
48 BAKER S, MATTHEWS I Lucas-Kanade 20 years on: a unifying framework[J]. International Journal of Computer Vision, 2004, 56 (3): 221- 255
doi: 10.1023/B:VISI.0000011205.11775.fd
49 BLACK M J, ANANDAN P The robust estimation of multiple motions: parametric and piecewise-smooth flow fields[J]. Computer Vision and Image Understanding, 1996, 63 (1): 75- 104
doi: 10.1006/cviu.1996.0006
50 TSUBAKI R, FUJITA I, TSUTSUMI S Measurement of the flood discharge of a small-sized river using an existing digital video recording system[J]. Journal of Hydro-environment Research, 2011, 5 (4): 313- 321
doi: 10.1016/j.jher.2010.12.004
51 金家莉 二维数字滤波在物理模型流场测量系统中的应用[J]. 水道港口, 2007, 28 (6): 448- 452
JIN Jia-li Application of 2D numerical filter for velocity measurement technique in flow field of physical models[J]. Journal of Waterway and Harbor, 2007, 28 (6): 448- 452
doi: 10.3969/j.issn.1005-8443.2007.06.014
52 高琪, 王成跃, 王洪平, 等 基于连续性条件的体PIV后处理技术[J]. 北京航空航天大学学报, 2013, (5): 693- 696
Gao Qi, WANG Cheng-yue, WANG Hong-ping, et al Post-processing of volumetric PIV data based on continuity condition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, (5): 693- 696
53 DRUAULT P, GUIBERT P, ALIZON F Use of proper orthogonal decomposition for time interpolation from PIV data[J]. Experiments in Fluids, 2005, 39 (6): 1009- 1023
doi: 10.1007/s00348-005-0035-3
54 姚叶. 基于深度学习的流场数据后处理方法的研究[D]. 北京: 北京邮电大学, 2018: 15-27.
YAO Ye. Research of flow field data postprocessing methods based on deep learning [D]. Beijing: Beijing University of Posts and Telecommunications, 2018: 15-27.
[1] 林爽,吴榕,王博,张子捷,魏坤腾. 涡轮叶片斜肋通道冷态流场特性的实验研究[J]. 浙江大学学报(工学版), 2022, 56(4): 823-832.
[2] 潘晓东,周廉默,孙宏磊,蔡袁强,史吏,袁宗浩. 基于粒子图像测速的高含水率软土真空预压试验[J]. 浙江大学学报(工学版), 2020, 54(6): 1078-1085.
[3] 赵亮, 吕亚飞, 贺治国, 林颖典, 胡鹏, 林挺. 分层水体和障碍物对斜坡异重流运动特性的影响[J]. 浙江大学学报(工学版), 2017, 51(12): 2466-2473.
[4] 付勇勇, 王旭航, 邓劲松, 叶自然, 周梦梦, 尤淑撑, 关涛. 采用国产GF-2遥感影像的复杂水网平原水体信息提取[J]. 浙江大学学报(工学版), 2017, 51(12): 2474-2480.
[5] 李飞, 朱鸿鹄, 张诚成, 施斌. 地基变形光纤光栅监测可行性的试验研究[J]. 浙江大学学报(工学版), 2017, 51(1): 204-211.
[6] 李学丰, 王奇, 王兴. 岩石细观裂隙组构的平面测定方法[J]. 浙江大学学报(工学版), 2016, 50(10): 2037-2044.
[7] 邵碧娟,李相鹏,李婷婷,王家德. 网板结构柱塞流电化学反应器流场的测试[J]. 浙江大学学报(工学版), 2015, 49(1): 130-135.
[8] 刘晨彬,潘颖,张海石,黄峰平,夏顺仁. 基于磁共振图像的脑瘤MGMT表达状况检测算法[J]. J4, 2012, 46(1): 170-176.
[9] 王宣银, 梁冬泰. 基于多元图像分析的表面缺陷检测算法[J]. J4, 2010, 44(3): 448-452.
[10] 张建伟,樊臻，颜钢锋,林志赟. 多自主移动机器人系统协调及合作控制实验平台[J]. J4, 2010, 44(11): 2124-2129.
[11] 白建基 郑水华 樊建人 岑可法. 雷诺数对气固两相圆湍射流影响的实验研究[J]. J4, 2006, 40(3): 433-437.
[12] 阮晓东 刘志皓 瞿建武. 粒子图像测速技术在两相流测量中的应用研究[J]. J4, 2005, 39(6): 785-788.