Surface wave measurements with IoT image processing

• Main Authors: Wei, Yuying, Sree, Dharma K. K., Yang, Chun, Law, Adrian Wing-Keung
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Civil engineering; Wave Profile; IoT System
• Online Access: https://hdl.handle.net/10356/155893
• Institution: Nanyang Technological University

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.