High-frequency oscillations and high surface aeration, induced by strong turbulence, make water depth measurement for hydraulic jumps a persistently challenging task. The investigation of hydraulic jump behaviour continues to be an important research theme, particularly 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 to mitigate certain limitations of existing depth measurement methods by adopting a non-intrusive computer vision-based approach to measuring the 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 ...

中文翻译：

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液压跳跃自由表面检测的图像处理：梯度/机器学习耦合模型

强湍流引起的高频振荡和高表面曝气使水力跃迁的水深测量一直是一项艰巨的任务。水力跳跃行为的研究仍然是一个重要的研究主题，特别是在静水盆设计方面。可靠地了解沿深度剖面的时间平均值和极值可帮助开发消音盆的适当设计，提高安全性并有助于理解跳变现象。本文提出了一种尝试，通过采用基于计算机视觉的非侵入式方法来测量水力跃迁的水深剖面，从而减轻了现有深度测量方法的某些局限性。所提出的方法分析视频数据以检测空气-水混合物和实验室水槽壁之间的边界。这是通过耦合两种计算机视觉方法来实现的：（1）垂直图像梯度分析，以及（2）通用...