A novel depth measurement method for urban flooding based on surveillance video images and a floating ruler

被引:3
|
作者
Liu, Shangkun [1 ]
Zheng, Wangguandong [1 ]
Wang, Xige [1 ]
Xiong, Huangrui [1 ]
Cheng, Jingye [2 ]
Yong, Cheng [1 ]
Zhang, Wentian [3 ]
Zou, Xiuguo [1 ]
机构
[1] Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China
[2] Nanjing Agr Univ, Coll Engn, Nanjing 210031, Peoples R China
[3] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
关键词
Urban flooding; Video image; Floating ruler; Object detection; Convolutional neural network; YOLOv5; ALGORITHM;
D O I
10.1007/s11069-023-06205-5
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In view of the low accuracy and high cost of existing urban flooding monitoring methods, this study proposed a method for measuring the depth of urban flooding using a combination of surveillance cameras and intentionally designed rulers. The method includes the binocular method and the subruler method, two implementation approaches based on a self-designed floating ruler to measure urban flooding depth. First, floating rulers were installed in the test area. Then, the pixel position of the floating rulers was obtained through surveillance cameras and the YOLOv5s object detection model. Finally, the water depth of urban flooding was obtained using two-pixel position to water depth transfer functions of the binocular method and the subruler method. The results showed that our proposed method precisely measured the depth of urban flooding in surveillance video frames, exhibiting an average relative measurement error of 8.541% and 5.250% and an average frame processing duration of 0.397 and 0.468 s. Compared with the existing method using machine vision and the binocular method, the subruler method can provide a low-cost and high-accuracy urban flooding monitoring solution, which has the potential for deployment in the field. The subruler method is recommended for use in areas prone to deep water accumulation.
引用
收藏
页码:1967 / 1989
页数:23
相关论文
共 50 条
  • [21] A Novel Depth-Based Virtual View Synthesis Method for Free Viewpoint Video
    Ahn, Ilkoo
    Kim, Changick
    IEEE TRANSACTIONS ON BROADCASTING, 2013, 59 (04) : 614 - 626
  • [22] Fast Prediction of Urban Flooding Water Depth Based on CNN-LSTM
    Chen, Jian
    Li, Yaowei
    Zhang, Shanju
    WATER, 2023, 15 (07)
  • [23] Advances in Urban Video-Based Surveillance Systems: A Survey
    Favorskaya, M.
    SOFT COMPUTING APPLICATIONS, (SOFA 2014), VOL 1, 2016, 356 : 87 - 102
  • [24] Urban waterlogging identification system based on public surveillance video
    Hu Peng
    Sun Weizhong
    Cai Yingqi
    Wu Guangsheng
    2020 INTERNATIONAL CONFERENCE ON IMAGE, VIDEO PROCESSING AND ARTIFICIAL INTELLIGENCE, 2020, 11584
  • [25] An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature
    He, Lixin
    Yang, Jing
    Kong, Bin
    Wang, Can
    APPLIED SCIENCES-BASEL, 2017, 7 (06):
  • [26] Vessel Detection and Tracking Method Based on Video Surveillance
    Wawrzyniak, Natalia
    Hyla, Tomasz
    Popik, Adrian
    SENSORS, 2019, 19 (23)
  • [27] Region-based fusion method for surveillance video
    Xiao, H. (ho.xiao@yahoo.cn), 2013, Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States (09):
  • [28] Identification of pedestrian submerged parts in urban flooding based on images and deep learning
    Jiang, Jingchao
    Feng, Xinle
    Huang, Jingzhou
    Chen, Jiaqi
    Liu, Min
    Cheng, Changxiu
    Liu, Junzhi
    Xue, Anke
    ENVIRONMENTAL MODELLING & SOFTWARE, 2025, 183
  • [29] Character Identifier Spotting Based on Deep Learning in Video Surveillance Images
    Feng, Chun
    Zhang, Huixiang
    Li, Xiaohui
    Liao, Kaihua
    Lu, Gaole
    2022 IEEE 10TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND NETWORKS (ICICN 2022), 2022, : 530 - 536
  • [30] A Novel Depth Estimation Method Using Infocused and Defocused Images
    Mahmoudpour, Saeed
    Kim, Manbae
    2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2014, : 123 - 124