Review of image-based river surface velocimetry research

doi:10.3785/j.issn.1008-973X.2021.09.017

Journal of ZheJiang University (Engineering Science)

2021, Vol. 55

Issue (9): 1752-1763 DOI: 10.3785/j.issn.1008-973X.2021.09.017

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

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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

Received: 16 September 2020 Published: 20 October 2021

CLC:

TP 391

Fund: 国网浙江省电力有限公司科技项目（5211JS18001200K3100000）

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	Dan YANG
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	Han-lin WANG
	Chao XU

Cite this article:

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.

URL:

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

基于图像的河流表面测速研究综述

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

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

Tab.1 Comparisons of image-based surface flow velocimetry method

Fig.1 Surface patern

Fig.2 Different band graph of river surface

Fig.3 Image orthorectification of river

Fig.4 Calculations of LSPIV

Fig.5 Correlation based velocity field estimation

Fig.6 Flow diagram of FFT-CC method

Fig.7 Illustration of WIDIM algorithm

Fig.8 Schematic diagram of spatio-temporal image

Fig.9 Speed line and space-time image

Fig.10 Flow diagram of LSPTV method

Fig.11 Principle of four-frame method

Fig.12 particle recognition and estimated flow in LSPTV

Fig.13 Statistic-based flow estimation

Fig.14 FAST-based OTV image filtered trajectories of river surface

Fig.15 Image of river surface and features from compressed sensing

Fig.16 Filtered flow velocity field using SSIV method


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