Two-stage robust surface detection algorithm for continuous casting mold water models using smooth surface-wave priors
Year: 2026
Type: article
Abstract: We propose a two-stage method that enables the accurate detection of water-surfaces from low-contrast images, aiming to quantify the water-surface dynamics in the water model of a continuous casting mold used in the steel manufacturing industry. In the first stage, the initial water-surface positions are estimated using Canny edge detection combined with dynamic programming. In the second stage, the surface estimation is refined by removing outliers through mathematical programming, which includes an l1 -loss function and a regularization term based on the sum of squared first/second-order differences. By explicitly considering the prior physical information of the smooth temporal and spatial variations of the water-surface, the method is robust against outliers and accurately detects water-surfaces even in cases where conventional approaches previously misidentified the surface. The proposed two-stage method—formulated with especially second-order difference regularization-is analyzed theoretically and validated through experiments conducted on an industrial-scale water model. It achieves near-real-time throughput and meets the practical accuracy requirement of ±1 mm, making it suitable for industrial deployment.
Authors: Hiroki Yamamoto, Daisuke Tsubakino
Institutions: Nippon Steel (Japan), Nagoya University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,235
OpenAlex ID: W7154740992