Binocular measurement method for a continuous casting slab based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting

Abstract

Due to the low measurement accuracy of the continuous casting slab model caused by difficulty in detecting ideal corners, a binocular measurement method based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting is proposed. First, the one-dimensional probabilistic Hough transform based on inclination angle voting and the Freeman chain code is used to detect the line segments of the exterior outline. Next, sub-pixel points are extracted in each region of interest (ROI) by using Zernike moments, and sifted in the overlapping area of adjacent ROIs. Then the orthogonal total least squares (TLS) method is applied to fitting sub-pixel edges. Finally, after the key points are matched, three-dimensional localization and measurement are completed according to the binocular vision measurement principle. The experimental results show that the minimum relative error and average relative error of length reach 0.3401% and 0.3945%, respectively, satisfying the measurement requirement. Compared with scale-invariant feature transform (SIFT) and the oriented FAST and rotated BRIEF (ORB), the measurement error of the proposed algorithm is reduced by 80.01% and 74.63%, respectively. Compared with another edge fitting method based on k-means clustering and least squares fitting, its measurement error is reduced by 34.11%, and the time consumption is shortened by 39.07%, verifying its excellent performance in accuracy and efficiency.