Typical Applications of Artificial Intelligence Video Recognition in Water Conservancy Digital Twin

ZHAO Ke-feng, CAO Hui-qun, LIN Li, JING Zheng, LUO Ping-an

Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (3) : 186-190.

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Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (3) : 186-190. DOI: 10.11988/ckyyb.20220825
KEY TECHNOLOGIES OF DIGITAL TWIN WATER CONSERVANCY PROJECTS

Typical Applications of Artificial Intelligence Video Recognition in Water Conservancy Digital Twin

  • ZHAO Ke-feng1,2, CAO Hui-qun1,2, LIN Li1,2, JING Zheng1,2, LUO Ping-an1,2
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Abstract

Video surveillance and artificial intelligence technologies are increasingly widely used in the construction of smart water conservancy. Digital twin construction is the key and core of building smart water conservancy. In line with the actual needs of water conservancy digital twin construction, we developed a video intelligent recognition model system using video monitoring, image processing, artificial intelligence and other technical means. With this system, intelligent simulation could interact with physical element videos. We highlight the systematic design of main application scenarios such as water gauge recognition, floating object recognition, surface water body detection and others. According to actual operation, the video intelligent recognition system can monitor, analyze, identify, predict and early warn physical objects continuously, hence has great application value in the intelligent construction of water conservancy.

Key words

artificial intelligence / digital twin / water gauge recognition / floating object recognition / surface water body detection

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ZHAO Ke-feng, CAO Hui-qun, LIN Li, JING Zheng, LUO Ping-an. Typical Applications of Artificial Intelligence Video Recognition in Water Conservancy Digital Twin[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(3): 186-190 https://doi.org/10.11988/ckyyb.20220825

References

[1] 孙维亚,王 达,许 帅,等.基于计算机视觉的水位检测算法[J].应用科学学报,2022,40(3):434-447.
[2] 程 诚,董晨龙,李 宏,等.智慧视频识别在水利信息化中的应用[J].四川水利,2019,40(3):124-128.
[3] 王 磊,陈明恩,孟凯凯,等.基于深度学习算法的水位识别方法研究[J].水利信息化,2020(3):39-43,56.
[4] 张铭飞,高国伟,胡敬芳,等.基于卷积神经网络的遥感图像水体提取[J].传感器与微系统,2022,41(1):72-74,88.
[5] 刘 瑶. 基于深度学习的多光谱遥感影像水体识别[D].南京:南京信息工程大学,2021.
[6] 屈慧慧,裴 亮,桑学锋,等.基于MNDWI特征空间的水体追踪识别方法研究[J].测绘工程,2021,30(2):32-35,44.
[7] 刘 伟,王源楠,江 山,等.基于Mask R-CNN的水面漂浮物识别方法研究[J].人民长江,2021,52(11):226-233.
[8] 李国进,姚冬宜,艾矫燕,等.基于改进YOLOv3算法的水面漂浮物检测方法[J].广西大学学报(自然科学版),2021,46(6):1569-1578.
[9] 徐 浩. 基于深度学习的水面漂浮物目标识别算法研究[D].上海:华东师范大学,2021.
[10] 高 强.基于深度学习的河道水面漂浮物检测研究[J].电子技术与软件工程,2021(18):127-128.
[11] 朱艳妮. 基于视觉分析的河道漂浮物检测与跟踪方法研究[D].杭州:浙江大学,2021.
[12] GOODFELLOW I,BENGIO Y,COURVILLE A. Deep Learning (Vol.1)[M].Cambridge:MIT Press,2016:326-366.
[13] LECUN Y, KAVUKCUOGLU K, FARABET C. Convolutional Networks and Applications in Vision[C]//DOI: 10.1109/ISCAS.2010.5537907.
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