This study develops two different approaches to perform temporal and spatial measurements of surface wave profile for experimental studies in transparent wave flumes. Both are based on image acquisition and processing with an Internet of Things (IoT) system consisting of three sets of GoPro camera cum Raspberry Pi connected wirelessly together in a local LAN. The first approach uses advanced edge algorithms with perspective transformation of the multiple cameras for the detection, while the second approach adopts Convolutional Neural Network (CNN) algorithms instead with training of the processed image data using information from additional discrete probes installed. Their accuracy is assessed under a range of experimental conditions of regular and irregular waves with different wave heights and periods, based on metrics that consist of the average errors of the predicted water surface profile as well as position errors for wave crests and troughs. The effects on the measurement accuracy due to the image acquisition frequency, camera resolution and camera location are also investigated. The results show that higher wave steepnesses generally lead to larger detection errors, and measurements for irregular waves are also more challenging. In addition, positioning the cameras closer to the wave flume sidewalls yields better detection results as expected, particularly in resolving wave crests and troughs, although the field of view narrows at the same time. However, higher video frequencies and camera resolutions might not necessarily improve the accuracy contrary to common expectation due to jaggedness in the image processing. Overall, both approaches are shown to be viable for the measurement of wave profile in the laboratory. The first approach is more straight forward in terms of implementation, and it performs well for regular wave conditions. The second approach requires more complex training of the neural networks, but its accuracy is significantly higher particularly for irregular waves.

本研究开发了两种不同的方法来对透明波水槽的实验研究进行表面波剖面的时间和空间测量。两者都基于物联网 (IoT) 系统的图像采集和处理，该系统由三组 GoPro 相机和 Raspberry Pi 在本地 LAN 中无线连接在一起。第一种方法使用具有多个摄像头透视变换的高级边缘算法进行检测，而第二种方法采用卷积神经网络 (CNN) 算法，而不是使用来自安装的附加离散探头的信息训练处理过的图像数据。它们的准确性是在具有不同波高和周期的规则和不规则波浪的一系列实验条件下评估的，基于由预测水面剖面的平均误差以及波峰和波谷的位置误差组成的指标。还研究了图像采集频率、相机分辨率和相机位置对测量精度的影响。结果表明，较高的波陡度通常会导致较大的检测误差，对不规则波的测量也更具挑战性。此外，将摄像机放置在更靠近波槽侧壁的位置会产生预期的更好检测结果，尤其是在解析波峰和波谷时，尽管视野同时变窄。然而，由于图像处理中的锯齿状，更高的视频频率和相机分辨率可能不一定会提高与普遍预期相反的准确度。总体，两种方法都被证明可用于实验室中的波形测量。第一种方法在实现方面更直接，并且在规则波浪条件下表现良好。第二种方法需要对神经网络进行更复杂的训练，但其精度明显更高，尤其是对于不规则波浪。