Article(id=1223201266193514735, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222376, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1668096000000, receivedDateStr=2022-11-11, revisedDate=1675699200000, revisedDateStr=2023-02-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563507840, onlineDateStr=2026-01-28, pubDate=1690214400000, pubDateStr=2023-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563507840, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563507840, creator=13701087609, updateTime=1769563507840, updator=13701087609, issue=Issue{id=1223201250133524577, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='7', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769563504012, creator=13701087609, updateTime=1769563583713, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223201584469885927, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223201584469885928, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=22, endPage=26, ext={EN=ArticleExt(id=1223201269649621266, articleId=1223201266193514735, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Water Level Identification Method of Rivers and Lakes Based on Mask RCNN, columnId=1223185960926564679, journalTitle=Water Resources and Power, columnName=HYDROLOGY，WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

With the development of deep learning and image recognition technology, monitoring the water level of urban rivers and lakes through video has become a hot research topic in recent years. In order to realize the comprehensiveness of urban river and lake water level monitoring, a method of river and lake water level identification based on Mask RCNN was proposed. The water level was obtained by the water level characters and their position relations in the video images, and it was verified by the monitoring video of the real water level station in Dongying City, Shandong Province. The results show that the probability that the comparison error between the water level identification result and the measured data was less than 2 cm was 68.5%, the probability of error less than 3 cm is 76.9%, the probability of error less than 5 cm is 93.5%, the average error is 2.1 cm, and the root mean square error (RMSE) is 3.0 cm, which meets the recognition accuracy requirements of video water gauge level in Technical Outline of Digital Twin Watershed Construction (Trial). Therefore, the model has a good recognition effect and a certain application prospect.

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随着深度学习和图像识别技术的发展，通过视频实现城市河湖水位监测成为近年来研究热点。为全面监测城市河湖水位，提出了基于Mask RCNN的河湖水位识别方法，通过视频画面中的水尺字符及其位置关系得到水尺水位，并使用山东省东营市的现实水位站点监控视频进行了验证。结果表明，在现实水位站点监控视频识别中，水尺水位识别结果与实测数据对比误差小于2、3、5 cm的概率分别为68.5%、76.9%、93.5%，平均误差为2.1 cm，均方根误差为3.0 cm，满足《数字孪生流域建设技术大纲（试行）》中对视频识别水尺水位的识别精度要求，故模型识别效果较好，且具有一定应用前景。

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徐志（1990-），男，博士、工程师，研究方向为降水监测、多源降水融合，E-mail：xuzhitc159@163.com

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徐志（1990-），男，博士、工程师，研究方向为降水监测、多源降水融合，E-mail：xuzhitc159@163.com

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