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.
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.
作者
杨聃
邵广俊
胡伟飞
刘国富
梁家铭
王瀚林
许超
YANG Dan;SHAO Guang-jun;HU Wei-fei;LIU Guo-fu;LIANG Jia-ming;WANG Han-lin;XU Chao(Jinshuitan Hydropower Plant of State Grid Zhejiang Electric Power Limited Company,Lishui 323000,China;College of Control Science and Engineering,Zhejiang University,Hangzhou 310058,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2021年第9期1752-1763,共12页
Journal of Zhejiang University：Engineering Science
基金
国网浙江省电力有限公司科技项目(5211JS18001200K3100000).
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
image velocimetry
river flow monitoring
river surface flow velocity
image analysis
large scale particle image velocimetry
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。
