Evaluation of deep learning computer vision for water level measurements in rivers

被引:3
|
作者
Liu, Wen-Cheng [1 ]
Huang, Wei-Che [1 ]
机构
[1] Natl United Univ, Dept Civil & Disaster Prevent Engn, Miaoli 360302, Taiwan
关键词
Deep learning; SegNet; Continuous image subtraction; Water level measurement; Image analysis; CAMERA IMAGES; RECOGNITION; MODELS;
D O I
10.1016/j.heliyon.2024.e25989
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Image -based gauging stations offer the potential for substantial enhancement in the monitoring networks of river water levels. Nonetheless, the majority of camera gauges fall short in delivering reliable and precise measurements because of the fluctuating appearance of water in the rivers over the course of the year. In this study, we introduce a method for measuring water levels in rivers using both the traditional continuous image subtraction (CIS) approach and a SegNet neural network based on deep learning computer vision. The historical images collected from onsite investigations were employed to train three neural networks (SegNet, U -Net, and FCN) in order to evaluate their effectiveness, overall performance, and reliability. The research findings demonstrated that the SegNet neural network outperformed the CIS method in accurately measuring water levels. The root mean square error (RMSE) between the water level measurements obtained by the SegNet neural network and the gauge station's readings ranged from 0.013 m to 0.066 m, with a high correlation coefficient of 0.998. Furthermore, the study revealed that the performance of the SegNet neural network in analyzing water levels in rivers improved with the inclusion of a larger number of images, diverse image categories, and higher image resolutions in the training dataset. These promising results emphasize the potential of deep learning computer vision technology, particularly the SegNet neural network, to enhance water level measurement in rivers. Notably, the quality and diversity of the training dataset play a crucial role in optimizing the network's performance. Overall, the application of this advanced technology holds great promise for advancing water level monitoring and management in river systems.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Improving landslide prediction by computer vision and deep learning
    Guerrero-Rodriguez, Byron
    Garcia-Rodriguez, Jose
    Salvador, Jaime
    Mejia-Escobar, Christian
    Cadena, Shirley
    Cepeda, Jairo
    Benavent-Lledo, Manuel
    Mulero-Perez, David
    INTEGRATED COMPUTER-AIDED ENGINEERING, 2024, 31 (01) : 77 - 94
  • [2] Deep Learning for Assistive Computer Vision
    Leo, Marco
    Furnari, Antonino
    Medioni, Gerard G.
    Trivedi, Mohan
    Farinella, Giovanni M.
    COMPUTER VISION - ECCV 2018 WORKSHOPS, PT VI, 2019, 11134 : 3 - 14
  • [3] Systematic Review of Emotion Detection with Computer Vision and Deep Learning
    Pereira, Rafael
    Mendes, Carla
    Ribeiro, Jose
    Ribeiro, Roberto
    Miragaia, Rolando
    Rodrigues, Nuno
    Costa, Nuno
    Pereira, Antonio
    SENSORS, 2024, 24 (11)
  • [4] Digitalized academic exam evaluation system, using deep learning and computer vision
    Espinoza, Angel
    Carlos Rangel, Jose
    2022 8TH INTERNATIONAL ENGINEERING, SCIENCES AND TECHNOLOGY CONFERENCE, IESTEC, 2022, : 215 - 222
  • [5] Deep Learning and Computer Vision for Estimating Date Fruits Type, Maturity Level, and Weight
    Faisal, Mohammed
    Albogamy, Fahad
    Elgibreen, Hebah
    Algabri, Mohammed
    Alqershi, Fattoh Abdu
    IEEE ACCESS, 2020, 8 : 206770 - 206782
  • [6] A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers
    Li, Gang
    Zhu, Haoyu
    Jian, Hongfu
    Zha, Wei
    Wang, Jiang
    Shu, Zhangkang
    Yao, Siyang
    Han, Huiming
    JOURNAL OF HYDROLOGY, 2023, 625
  • [7] ChainerCV: a Library for Deep Learning in Computer Vision
    Niitani, Yusuke
    Ogawa, Toru
    Saito, Shunta
    Saito, Masaki
    PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), 2017, : 1217 - 1220
  • [8] A Survey of the Application of Deep Learning in Computer Vision
    Liu Yuexia
    Cheng Yunfei
    Wang Wu
    GLOBAL INTELLIGENCE INDUSTRY CONFERENCE (GIIC 2018), 2018, 10835
  • [9] Applications and Challenges of Deep Learning in Computer Vision
    Singh, Chetanpal
    HEALTH INFORMATION SCIENCE, HIS 2021, 2021, 13079 : 223 - 233
  • [10] Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows
    Devi, Indu
    Singh, Naseeb
    Dudi, Kuldeep
    Ranjan, Rakesh
    Lathwal, Surender Singh
    Tomar, Divyanshu Singh
    Nagar, Harsh
    SMART AGRICULTURAL TECHNOLOGY, 2024, 8