To address the low measurement accuracy resulting from the inability to detect ideal corners of an object， a binocular-vision-based measurement method incorporating the one-dimensional probabilistic Hough transform and local Zernike moment is proposed herein. First， the one-dimensional probabilistic Hough transform is used for line detection of the outer contour. Next， sub-pixel extraction is performed using the Zernike moment method in the region of interest （ROI） established according to the line detection， and sub-pixel points are screened in the intersection region of the ROI. Then， before matching the key points， sub-pixel edge lines are fitted using the orthogonal total least squares method. Finally， the three-dimensional spatial information of a continuous casting slab model is obtained via the triangulation principle， and the measurement is completed. Here， the continuous casting slab model is considered as the measurement object. Experimental results indicate that the minimum relative error of the proposed algorithm is 0.340 1%， which satisfies the measurement requirement. The average relative error in the length is 0.3945%， which is 80.01% and 74.63% smaller than those of the traditional SIFT and ORB algorithms， respectively. Compared with another method based on edge fitting， the measurement error and time consumption of the proposed algorithm are reduced by 34.11% and 39.07%， respectively， confirming its measurement accuracy and efficiency.