Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV
As an important part of hydrometry, river discharge monitoring plays an irreplaceable role in the planning and management of water resources and is an essential element and necessary means of river management. Due to its benefits of simplicity, efficiency and safety, Space-Time Image Velocimetry (ST...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/955 |
| _version_ | 1827622043881832448 |
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| author | Jianghuai Lu Xiaohong Yang Jianping Wang |
| author_facet | Jianghuai Lu Xiaohong Yang Jianping Wang |
| author_sort | Jianghuai Lu |
| collection | DOAJ |
| description | As an important part of hydrometry, river discharge monitoring plays an irreplaceable role in the planning and management of water resources and is an essential element and necessary means of river management. Due to its benefits of simplicity, efficiency and safety, Space-Time Image Velocimetry (STIV) has attracted attention from all around the world. The most crucial component of the STIV is the detection of the Main Orientation of Texture (MOT), and the precision of detection directly affects the results of calculations. However, due to the complicated river flow characteristics and the harsh testing environment in the field, a large amount of noise and interfering textures show up in the space-time images, which affects the detection results of the MOT. In response to the shortage of noise and interference texture, a new non-contact image analysis method is developed. Firstly, Multi-scale Retinex (MSR) is proposed to pre-process the images for contrast enhancement; secondly, a fourth-order Gaussian derivative steerable filter is employed to enhance the structure of the texture; next, based on the probability density distribution function and the orientations of the enhanced images, the noise suppression function and the orientation-filtering function are designed to filter out the noise to highlight the texture. Finally, the Fourier Maximum Angle Analysis (FMAA) is used to filter out the noise further and obtain the clear orientations to achieve the measurement of velocity and discharge. The experimental results show that, compared with the widely used image velocimetry measurements, the accuracy of our method in the average velocity and flow discharge is significantly improved, and the real-time performance is excellent. |
| first_indexed | 2024-03-09T11:15:42Z |
| format | Article |
| id | doaj.art-3832178350c44c1b8f090a92f565606c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:15:42Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3832178350c44c1b8f090a92f565606c2023-12-01T00:30:24ZengMDPI AGSensors1424-82202023-01-0123295510.3390/s23020955Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIVJianghuai Lu0Xiaohong Yang1Jianping Wang2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaAs an important part of hydrometry, river discharge monitoring plays an irreplaceable role in the planning and management of water resources and is an essential element and necessary means of river management. Due to its benefits of simplicity, efficiency and safety, Space-Time Image Velocimetry (STIV) has attracted attention from all around the world. The most crucial component of the STIV is the detection of the Main Orientation of Texture (MOT), and the precision of detection directly affects the results of calculations. However, due to the complicated river flow characteristics and the harsh testing environment in the field, a large amount of noise and interfering textures show up in the space-time images, which affects the detection results of the MOT. In response to the shortage of noise and interference texture, a new non-contact image analysis method is developed. Firstly, Multi-scale Retinex (MSR) is proposed to pre-process the images for contrast enhancement; secondly, a fourth-order Gaussian derivative steerable filter is employed to enhance the structure of the texture; next, based on the probability density distribution function and the orientations of the enhanced images, the noise suppression function and the orientation-filtering function are designed to filter out the noise to highlight the texture. Finally, the Fourier Maximum Angle Analysis (FMAA) is used to filter out the noise further and obtain the clear orientations to achieve the measurement of velocity and discharge. The experimental results show that, compared with the widely used image velocimetry measurements, the accuracy of our method in the average velocity and flow discharge is significantly improved, and the real-time performance is excellent.https://www.mdpi.com/1424-8220/23/2/955hydrometryvelocity measurementSTIVMOTFMAA |
| spellingShingle | Jianghuai Lu Xiaohong Yang Jianping Wang Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV Sensors hydrometry velocity measurement STIV MOT FMAA |
| title | Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV |
| title_full | Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV |
| title_fullStr | Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV |
| title_full_unstemmed | Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV |
| title_short | Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV |
| title_sort | velocity vector estimation of two dimensional flow field based on stiv |
| topic | hydrometry velocity measurement STIV MOT FMAA |
| url | https://www.mdpi.com/1424-8220/23/2/955 |
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