GraFar - Repository of the Faculty of Civil Engineering
Faculty of Civil Engineering of the University of Belgrade
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   GraFar
  • GraFar
  • Катедра за хидротехнику и водно-еколошко инжењерство
  • View Item
  •   GraFar
  • GraFar
  • Катедра за хидротехнику и водно-еколошко инжењерство
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model

Thumbnail
2020
RLjubicic_et_al-Image_processing_for_HJ_2020-04-11.pdf (1.574Mb)
Authors
Ljubičić, Robert
Vićanović, Ivana
Zindović, Budo
Kapor, Radomir
Savić, Ljubodrag
Article (Accepted Version)
,
IOP
Metadata
Show full item record
Abstract
High-frequency oscillations and high surface aeration, induced by the strong turbulence, make water depth measurement for hydraulic jumps a persistently challenging task. The investigation of the hydraulic jump behaviour persists as an important research theme, especially with regards to the stilling basin design. Reliable knowledge of time-averaged and extreme values along a depth profile can help develop an adequate design of a stilling basin, improve safety and aid the understanding of the jump phenomenon. This paper presents an attempt of mitigating certain limitations of existing depth measurement methods by adopting a non-intrusive computer vision-based approach to measuring water depth profile of a hydraulic jump. The proposed method analyses video data in order to detect the boundary between the air-water mixture and the laboratory flume wall. This is achieved by coupling two computer vision methods: (1) analysis of the vertical image gradients, and (2) general-purpose edge det...ection using a deep neural network model. While the gradient analysis technique alone can provide adequate results, its performance can be significantly improved in combination with a neural network model which incorporates a "human-like" vision in the algorithm. The model coupling reduces the likelihood of false detections and improves the overall detection accuracy. The proposed method is tested in two experiments with different degrees of jump aeration. Results show that the coupled model can reliably and accurately capture the instantaneous depth profile along the jump, with low sensitivity to image noise and flow aeration. The coupled model presented fewer false detections than the gradient-based model, and offered consistent performance in regions of high, as well as low aeration. The proposed approach allows for automated detection of the free-surface interface and expands the potential of computer vision-based measurement methods in hydraulics.

Keywords:
hydraulic jump / depth measurement / stilling basins / non-intrusive measurement / computer vision / image processing
Source:
Measurement Science and Technology, 2020
Publisher:
  • IOP
Funding / projects:
  • Urban Drainage Systems as Key Infrastructure in Cities and Towns (RS-37010)
  • Monitoring and Modeling of Rivers and Reservoirs (MORE) - Physical, Chemical, Biological and Morphodynamic Parameters (RS-37009)

DOI: 10.1088/1361-6501/ab8b22

ISSN: 0957-0233

WoS: 000553406700001

Scopus: 2-s2.0-85092624335
[ Google Scholar ]
URI
https://grafar.grf.bg.ac.rs/handle/123456789/1976
Collections
  • Катедра за хидротехнику и водно-еколошко инжењерство
  • Катедра за материјале и конструкције
Institution/Community
GraFar
TY  - JOUR
AU  - Ljubičić, Robert
AU  - Vićanović, Ivana
AU  - Zindović, Budo
AU  - Kapor, Radomir
AU  - Savić, Ljubodrag
PY  - 2020
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1976
AB  - High-frequency oscillations and high surface aeration, induced by the strong turbulence, make water depth measurement for hydraulic jumps a persistently challenging task. The investigation of the hydraulic jump behaviour persists as an important research theme, especially with regards to the stilling basin design. Reliable knowledge of time-averaged and extreme values along a depth profile can help develop an adequate design of a stilling basin, improve safety and aid the understanding of the jump phenomenon. This paper presents an attempt of mitigating certain limitations of existing depth measurement methods by adopting a non-intrusive computer vision-based approach to measuring water depth profile of a hydraulic jump. The proposed method analyses video data in order to detect the boundary between the air-water mixture and the laboratory flume wall. This is achieved by coupling two computer vision methods: (1) analysis of the vertical image gradients, and (2) general-purpose edge detection using a deep neural network model. While the gradient analysis technique alone can provide adequate results, its performance can be significantly improved in combination with a neural network model which incorporates a "human-like" vision in the algorithm. The model coupling reduces the likelihood of false detections and improves the overall detection accuracy. The proposed method is tested in two experiments with different degrees of jump aeration. Results show that the coupled model can reliably and accurately capture the instantaneous depth profile along the jump, with low sensitivity to image noise and flow aeration. The coupled model presented fewer false detections than the gradient-based model, and offered consistent performance in regions of high, as well as low aeration. The proposed approach allows for automated detection of the free-surface interface and expands the potential of computer vision-based measurement methods in hydraulics.
PB  - IOP
T2  - Measurement Science and Technology
T1  - Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model
DO  - 10.1088/1361-6501/ab8b22
ER  - 
@article{
author = "Ljubičić, Robert and Vićanović, Ivana and Zindović, Budo and Kapor, Radomir and Savić, Ljubodrag",
year = "2020",
abstract = "High-frequency oscillations and high surface aeration, induced by the strong turbulence, make water depth measurement for hydraulic jumps a persistently challenging task. The investigation of the hydraulic jump behaviour persists as an important research theme, especially with regards to the stilling basin design. Reliable knowledge of time-averaged and extreme values along a depth profile can help develop an adequate design of a stilling basin, improve safety and aid the understanding of the jump phenomenon. This paper presents an attempt of mitigating certain limitations of existing depth measurement methods by adopting a non-intrusive computer vision-based approach to measuring water depth profile of a hydraulic jump. The proposed method analyses video data in order to detect the boundary between the air-water mixture and the laboratory flume wall. This is achieved by coupling two computer vision methods: (1) analysis of the vertical image gradients, and (2) general-purpose edge detection using a deep neural network model. While the gradient analysis technique alone can provide adequate results, its performance can be significantly improved in combination with a neural network model which incorporates a "human-like" vision in the algorithm. The model coupling reduces the likelihood of false detections and improves the overall detection accuracy. The proposed method is tested in two experiments with different degrees of jump aeration. Results show that the coupled model can reliably and accurately capture the instantaneous depth profile along the jump, with low sensitivity to image noise and flow aeration. The coupled model presented fewer false detections than the gradient-based model, and offered consistent performance in regions of high, as well as low aeration. The proposed approach allows for automated detection of the free-surface interface and expands the potential of computer vision-based measurement methods in hydraulics.",
publisher = "IOP",
journal = "Measurement Science and Technology",
title = "Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model",
doi = "10.1088/1361-6501/ab8b22"
}
Ljubičić, R., Vićanović, I., Zindović, B., Kapor, R.,& Savić, L.. (2020). Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model. in Measurement Science and Technology
IOP..
https://doi.org/10.1088/1361-6501/ab8b22
Ljubičić R, Vićanović I, Zindović B, Kapor R, Savić L. Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model. in Measurement Science and Technology. 2020;.
doi:10.1088/1361-6501/ab8b22 .
Ljubičić, Robert, Vićanović, Ivana, Zindović, Budo, Kapor, Radomir, Savić, Ljubodrag, "Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model" in Measurement Science and Technology (2020),
https://doi.org/10.1088/1361-6501/ab8b22 . .

DSpace software copyright © 2002-2015  DuraSpace
About the GraFar Repository | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceCommunitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About the GraFar Repository | Send Feedback

OpenAIRERCUB