Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model
| العنوان: | Image processing for hydraulic jump free-surface detection: coupled gradient/machine learning model |
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| المؤلفون: | Robert Ljubičić, Ivana Vicanovic, Ljubodrag Savić, Radomir Kapor, Budo Zindović |
| المصدر: | Measurement Science and Technology |
| بيانات النشر: | IOP Publishing, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | depth measurement, Computer science, business.industry, non-intrusive measurement, Applied Mathematics, hydraulic jump, 0207 environmental engineering, Image processing, 02 engineering and technology, stilling basins, 01 natural sciences, computer vision, image processing, 010305 fluids & plasmas, Free surface, 0103 physical sciences, Measured depth, Computer vision, Artificial intelligence, 020701 environmental engineering, business, Instrumentation, Engineering (miscellaneous), Hydraulic jump |
| الوصف: | 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. |
| تدمد: | 1361-6501 0957-0233 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71b67a6472fff257b8ce32bcbd412ce2 https://doi.org/10.1088/1361-6501/ab8b22 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....71b67a6472fff257b8ce32bcbd412ce2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13616501 09570233 |
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