Measuring Scour Level using Image Processing
Abstract
Scour monitoring is a process to measure the level of soil erosion at the bridge pillars. Currently, the monitoring and the interpretation is done manually. This work proposes an automatic scour monitoring system that is able to detect and measure the level of scour. The system uses image processing techniques such as image inpainting, Hough transform to detect the level of scour, and artificial neural network to measure the scour level and scale numbers. Results show that the scour level can be detected automatically for even and uneven soil, and the scour level can be measured automatically and accurately.
Proposed System
This paper presents a development of an automatic scour level measuring system using image processing techniques for the even and uneven sediment. The proposed technique provides an automatic measuring of the scour level to avoid inconsistency of human observers. The scour monitoring system consists of works involving coastal engineering and image processing The proposed image processing of scour monitoring system consist of a number of techniques, such as image inpainting, image subdivision, Hough transform and character recognition using neural network In order to focus on the measurement of the sediment level, it is easier to remove the scale numbers and the scale levels than to retain them in the image. Here, Cahn-Hilliard image inpainting [24,25] is used to remove the scale numbers and levels. Image inpainting is a technique to fill in missing or damaged regions of image using pixel intensities surrounding the regions.
CONCLUSION
A computerized scour monitoring system based on image processing techniques was developed. The scour that has variation in structure and steepness can be measured using the proposed technique. Results of this work show that the level of scour can be measured automatically with higher accuracy than that of conventional approach. The scale numbers and the scale levels can also be detected. In the future work, the evolution of the levels of sediment will be measured and monitored in time-series.
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