An Automatic Detection Algorithm of River Surface Velocity Estimation

Abstract

In the 21st century, we are deeply impacted by global warming and extremely climate change. Meanwhile, relative organizations around the world carry out disaster prevention research. Since along with the tendency to enhance disaster occurred, the number of relevant monitoring system is used along with the growth of the field of disaster prevention. However, there are several limitations in the system of conventional monitors. For example the traditional monitor system usually needs people to keep an eye on those disasters, which makes the changes of disasters cannot be passed instantly and correctly. This paper will propose a set of image algorithms for disaster analysis detection technology. Through the non-contact measurement mode, analyze information of rivers in Taiwan and extract crucial information of river surface velocity in hydrological disaster prevention. With driftwood in rivers, bubble waves, swirling waves and other changes of images, using Retinex theory and correlation with related technical principles of the application, it will overcome the interference effect of light and shadows in harsh environments. This mode can also correctly estimate instantaneous surface velocity of the river, and through experiment of several images information to verify the river surface velocity by the instrument in the same time, confirmed the development of this system has its value and reliability. It is expected to achieve effectiveness of real time information detection and automatic notification system of disaster prevention.

Real-time Liquid Level and color Detection system using Image Processing

“…These sensors may cost a lot and may require direct contact with liquid like pressure sensor, which is not suitable for chemical reaction (Manashti & Azimi, 2013) (KHARADE, GENDLE, LODHA, & JADHAV, 2017). Water level detection based on image processing was proposed by couple of papers to measure the level of water in reservoir using surveillance cameras for flood monitoring (Hasan, et al, 2016) (Lin & Sun, 2016) (Manashti & Azimi, 2013).These detection methods were design to measure the water level in reservoir. Therefore, it is not very sensitive to the small changes in the water level.…”