基于图像语义分割的水位智能监测方法  

Intelligent water-level monitoring method based on image semantic segmentation

作　　者：张文静 张振[1] 黄剑 周扬 蒋芸 ZHANG Wenjing;ZHANG Zhen;HUANG Jian;ZHOU Yang;JIANG Yun(College of Computer and Information Engineering,Hohai University,Nanjing 211100,China;Hydrology and Water Resources Monitoring Center of the Middle Reaches of Ganjiang River,Ji’an 343000,China)

机构地区：[1]河海大学计算机与信息学院,江苏南京211100 [2]赣江中游水文水资源监测中心,江西吉安343000

出　　处：《河海大学学报（自然科学版）》2023年第5期24-30,共7页Journal of Hohai University(Natural Sciences)

基　　金：江苏省水利科技项目(2021070);中国博士后科学基金面上项目(2019M651673);浙江省水利厅科技计划项目(RB2037);中央高校基本科研业务费专项经费资助项目(B200202187)。

摘　　要：为了解决现有基于灰度图像分割的水位线检测方法易受水面耀光、倒影等复杂光照条件的影响,且在高洪期水尺易被漂浮物缠绕引起测量粗大误差的问题,设计了一种基于深度学习的水尺水位智能监测方法。该方法采用不同条件下采集并由人工精确标注的水尺、水面和漂浮物三分类样本图像构建数据集,训练深层全卷积神经网络,实现了对水尺图像的逐像素分类预测,最终在语义分割图像中检测水位线的像素位置,将其转化为实际水位值。试验结果表明:该方法能够克服传统方法在图像特征提取方面的不足,提升图像分割对野外复杂变化环境的适应性,实现测量有效性的识别,达到水尺水位智能监测的目的,测量的综合不确定度小于3.cm。To address the problem that the existing water-level detection method based on the gray image segmentation is susceptible to complex illumination conditions such as water surface flaring and reflection,and the problem of large measurement errors caused by the entanglement of floating objects during high flood periods,a deep-learning-based intelligent water-level monitoring method for staff gauge is designed.This method uses the three-class sample images of staff gauge,water surface,and floating objects,which are collected under different conditions and accurately labeled manually,to construct the data set,and then trains the deep fully convolutional neural network to perform the pixel-by-pixel classification prediction of staff gauge images.Finally,the pixel position of water line is detected in the semantic segmentation image,and transformed into the actual water level value.The test results show that the method can overcome the shortcoming of traditional methods in the image feature extraction,improve the adaptability of image segmentation to complex changing environments in the field,realize the recognition of measurement effectiveness,and achieve the purpose of intelligent monitoring of water level with the comprehensive uncertainty of measurement less than 3 cm.

关 键 词：水位测量 深度学习 图像处理 人工智能 智慧水利 

分 类 号：P332.3[天文地球—水文科学]