Waterline Detection and Water Level Estimation based on HED Edge Detection
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Punn Kiriwong, Jidapa Thongnirun, Wipada Glahan, Supakorn Siddhichai, Kitti Koonsanit, Phitchakorn Watcharanurak, Santitham Prom-on and Kharittha Jangsamsi
Publication year: 2024
URL: https://jcsse2024.computing.psu.ac.th/
Abstract
Due to the flooding problem which has a significant challenge towards locals who are responsible for monitoring the water level in various canals, an innovative solution for monitoring water levels is needed. Therefore, a method utilizing images from CCTV cameras for real-time water level assessment is presented. By leveraging an image processing approach including image restoration and binary image processing techniques, we utilized the HED edge detection followed by erosion to detect water lines and then estimate the water level through the comparison and matching the coordinates between detected edges and predefined water levels. Experimental results compared to traditional edge detection like Canny, and deep learning based HED edge detection proved the effectiveness of our proposed method in various challenging canal scenarios has exceeded the accuracy of the other two methods.
Keywords
Image processing