摘要
详细精准的水深及流速监测信息对城市洪涝救灾抢险和风险评估具有重要意义。随着极端气候事件的频发和城市化进程的推进,传统的城市洪涝监测方法已难以满足区域化、实时化的监测需求。针对目前城市洪涝仍缺乏整合水深流速为一体的区域监测方法的问题,研究构建了基于双目视觉的城市洪涝区水深及流速监测系统,通过覆盖同一视野的两台监控摄像头进行洪涝区域影像数据采集,实现水深及流速的高精度测量;提出了基于特征点提取匹配的洪涝区水深重建方法,利用双目视觉的视差原理,通过提取匹配洪涝区域特征点计算三维坐标,重建水深分布;提出了基于漂浮物识别并耦合水深分布的表面流场测量方法,通过识别洪涝表面的漂浮物(如树叶、垃圾等)计算洪水表面流速,耦合水深分布,实现水深流速一体化监测。为验证监测方法的可行性,研究在实验室内搭建了水槽模拟洪涝环境,并利用监控摄像头拍摄洪涝影像进行水深及流速计算。结果表明:与波高仪相比,水深测量误差约为2.7 mm,与粒子测速技术相比,流速测量误差在5%左右。相较于传统监测方法,本方法具有非接触、无风险、区域监测、就地取材、无需额外增添设备等优点。
Detailed and accurate monitoring of water depth and flow velocity is essential for effective urban flood rescue operations and risk assessment.Due to the increasing frequency of extreme weather events and the advancement of urbanization,traditional urban flood monitoring methods are no longer sufficient for regional and real-time monitoring.To address the lack of integrated regional monitoring methods for water depth and velocity in urban floods,this study developed a binocular vision-based monitoring system for urban flood areas.Two monitoring cameras with overlapping fields of view were employed to capture flood area images,enabling high-precision measurements of water depth and flow velocity.A flood area water depth reconstruction method based on feature points extraction and matching was proposed.Using the parallax principle of binocular vision,matching feature points in flood areas were extracted to calculate threedimensional coordinates,thereby reconstructing the water depth distribution.A surface flow field measurement method based on the identification of floating objects coupled with the water depth distribution was also proposed.By identifying floating objects(such as leaves,debris,etc.)on the flood surface,the surface velocity is calculated and integrated with water depth distribution,enabling comprehensive monitoring of both water depth and velocity.To validate the feasibility of the method,a laboratory water tank was constructed to simulate flood conditions,and monitoring cameras were used to capture images for calculating water depth and velocity.The experimental results show that the water depth error is approximately 2.7 mm compared to wave probes,and the flow velocity error remains within 5%relative to particle velocity measurements.Compared to traditional monitoring techniques,this approach offers non-contact operation,reduced risk,regional monitoring capabilities,and eliminates the need for additional equipment.
作者
张欣茹
王波
ZHANG Xin-ru;WANG Bo(State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,Sichuang Province,China)
出处
《中国农村水利水电》
北大核心
2025年第7期101-107,共7页
China Rural Water and Hydropower
基金
四川省科技计划资助项目(2023YFS0440)。
关键词
双目视觉
监控摄像头
城市洪涝
水深
流速
监测方法
binocular vision
monitoring camera
urban flooding
water depth
flow velocity
monitoring method
