Measurement of debris flow velocity in flume using normal image by space-time image velocimetry incorporated with machine learning

Various methods have been applied to the velocity measurement in the soil and rocks waterway experiment. Although most velocity measurements use observation sensors, soil and rocks are two-phase flow in which water and soil are mixed, and the velocity measurement method using direct contact with soil and sensors has various problems, including the potential for equipment damage and noise. Therefore, a velocity measurement method that employs high-resolution image analysis using a high-speed camera, which is an indirect velocity measurement method, has been proposed. Although the measurement of the velocity using high-resolution image analysis is associated with a very little risk of equipment damage, several problems have been reported, such as expensive system construction costs, analysis methods to eliminate noise present in images, and the low accuracy of the velocity measurement according to the slope (velocity) evaluation by space-time image (STI), indicating the movement velocity for each frame (moving distance). This study aims to measure the velocity of soil and rocks by applying space-time image velocimetry (STIV), which has not yet been applied to the soil and rocks waterway experiment. To evaluate the STIV performance, an indoor waterway experiment was carried out, and the results were compared with the velocity measured using a general-purpose high-speed camera. Machine learning was applied to the STI slope estimation method to improve the performance of the velocity measurement according to the noise interference included in the image. The use of convolutional neural networks (CNNs) is a useful way of extracting the characteristics of images, and they have learned to detect slopes using STI's 2D Fourier transform images. As a result, the performance of the velocity evaluation was significantly improved. Furthermore, it is possible to easily measure the velocity with high accuracy by freely setting an area to be measured in the captured image, such as the approaching velocity of soil and rocks or the overflow velocity at a debris barrier.

Automated summary

This study aims to measure the velocity of soil and rocks by applying space-time image velocimetry (STIV), which has not yet been applied to the soil and rocks waterway experiment. The performance of the velocity evaluation was significantly improved by applying machine learning to the STI slope estimation method, and the velocity can be easily measured with high accuracy by freely setting an area to be measured in the captured image.