Deep learning video analysis as measurement technique in physical models

Abstract

In coastal engineering context, the use of video imagery is widespread. Especially in field settings along sandy coasts, many types of data have been derived from video imagery, often using non-learning analysis techniques. Recent advances in the field of computer vision and deep learning allow for the automation of image segmentation. In this paper, these techniques are combined with video imagery of physical model tests, resulting in an innovative non-intrusive measurement technique. This technique is validated for three different applications: the measurement of surface elevation, wave run-up and bed level development. In addition to demonstrating its potential as an alternative for existing measurement instruments, it is shown that the added detail in the spatial or temporal domain provided by the technique can lead to new insights. Examples of this are the detailed analysis on the variability of the run-up height over the width of the flume and the spatial distribution of run-up velocities over the slope.