Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV

Lu, Jianghuai; Yang, Xiaohong; Wang, Jianping

doi:10.3390/s23020955

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The texture details of the image processed by MSDB have been enhanced, and the image quality has been improved. Based on this, Gaussian direction stretching filtering (GDSF) [25,26] is further used for image enhancement preprocessing.…”

Section: Gaussian Directional Stretch Filteringmentioning

confidence: 99%

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

Yuan,

Che,

Wang

et al. 2024

Meas. Sci. Technol.

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River flow monitoring serves as a crucial data source in hydrological stations, employing the widely adopted space-time image velocimetry (STIV) method both domestically and internationally. However, current methods yield space-time image trajectories with limited usable information, particularly due to the interference of complex flow characteristics and harsh outdoor environments in natural rivers. This interference leads to errors in detecting the main orientation of texture, consequently reducing measurement accuracy. To overcome these challenges, this paper proposes an river video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics. The Lucas-Kanade tracking algorithm is employed to track velocity points and generate tracking trajectories. These trajectories' coordinates are then fitted into straight lines to automatically arrange velocity-measuring lines in natural rivers. The algorithm leverages multi-scale detail Boosting and Gaussian directional stretch filtering to enhance the quality and texture trajectories of space-time images. Furthermore, background noise filtering and stripe noise filtering are applied to denoise the space-time images, effectively mitigating the impact of noise caused by the natural river environment and camera equipment. The proposed algorithm incorporates projection statistical characteristics algorithms to accurately detect the dominant texture direction, enabling precise calculation of Mean Velocity and cross-sectional discharge of the river. Experimental validation was conducted in both natural and regular river channels, demonstrating the superior performance of the proposed solution compared to widely used LSPIV, STIV, MobileNet-STIV and FD-DIS-G algorithms. The algorithm exhibited higher accuracy, stability, and applicability, with errors at each velocity-measuring point below 10%, and relative errors of the calculated Mean Velocity and cross-sectional discharge below 3%. This affirms the algorithm's superior accuracy, stability, and applicability.

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Section: Gaussian Directional Stretch Filteringmentioning

confidence: 99%

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

Yuan,

Che,

Wang

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…Commonly used software includes particle image velocimetry (PIV) tools such as PIVlab [11], OpenPIV [12], and Fudaa-LSPIV [13,14]. Additionally, other algorithms like Kanade Lucas Tomasi (KLT) are utilized, with KLT-IV [15] being one of the notable tools, as well as space time image velocimetry (STIV) [16][17][18][19][20] represented by RIVeR-STIV (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Wijaya,

Liu,

Suharyanto

et al. 2023

Water

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The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

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