## 1 Introduction  
With the continuous improvement of the shape and quality requirements for sheet and strip materials in the steel industry [1], straightening by tension bending is an important process for strip straightening, aiming to improve board shape [2]. Previous research on strip steel has mainly focused on issues such as warping and waviness, while there has been limited study on accurate measurement of strip dimensions during the straightening process in cross-cutting units. If there are significant deviations in strip dimensions, it can have a major impact on subsequent products and may result in scrap of cross-cut boards.  
In the past, the sizing of board strips in the cross-cutting machine unit was mainly measured through manual sampling during shutdowns, which could only obtain accurate dimensions for some boards and could not reflect the sizes of all boards. This not only consumed a large amount of manpower and resources but also severely restricted production efficiency. Recently, with the rapid development of machine vision technology, its advantages such as fast detection speed, high accuracy, and strong robustness [3; 4] have been widely popularized and applied in industrial measurement.  
On the hot rolling mill, [PERSON] [5] introduced a kind of strip slab arc machine vision measuring system, providing the basis for the slab radian control; the machine vision method it proposed has significant advantages in non-contact accuracy and real-time control, but the algorithm efficiency and multi-sensor fusion capability need to be further optimized. [PERSON]'s [6] system, based on machine vision, for measuring the length of the large hot forging systematically evaluated the plate shape control ability of the hot rolling mill through the finite element method. Its theoretical analysis provided an important reference for the design and parameter optimization of the rolling mill, but there were limitations in the model dimension, dynamic control, and industrial verification. [PERSON]'s [7] method, based on surface defect data augmentation (SDDA) enhancement of cold rolled steel sheet surface defect data, is a data augmentation method that has significant advantages in small sample processing and multimodal feature fusion, but the efficiency and environmental adaptability of the generative model need to be further optimized. [PERSON] [8] put forward a kind of DIC based on the convolution neural network (CNN) strain field image semantic segmentation technique; this method has significant advantages in the accuracy of automated detection and adaptability to complex damages, but the computational efficiency and generalization ability need to be further optimized. [PERSON] [9] proposed a system based on machine vision technology and image detection and non-contact online measurement for strip deviation; the core advantage of this system lies in non-contact, high-precision, and real-time measurement, but it has limitations in terms of environmental adaptability, computational efficiency, and data dependence. [PERSON] [10] proposed a computationally efficient PBF-LB/M thermal modeling method; through the innovative combination of enthalpy methods and multi-scale strategies, it provides an efficient simulation tool for thermal management in additive manufacturing. Its core advantage lies in the balance between computational efficiency and accuracy. However, model simplification and dynamic adaptability remain the main limitations. In the future, data-driven methods and multi-physics coupling technologies need to be integrated to address more complex industrial demands.  
In this paper, an online real-time measurement system with fast and high accuracy is proposed. The size of the cross-cutting board straightening is based on visual inspection, which achieves online real-time measurement of the length, width, and diagonal of the cross-cutting board. Compared with the above-mentioned work, this measurement system has high computational efficiency, low missed detection rate, and higher robustness.