Online deviation measurement system of the strip in the finishing process based on machine vision

Yan, Shuzong; Wang, Xiaochen; Yang, Quan; Xu, Dong; He, Haoxiang; Liu, Yang

doi:10.1016/j.measurement.2022.111735

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…The camera acquires the light bar images at different positions and obtains the 3D coordinates of the light bar position after the light bar center extraction and perspective transformation. Many scholars worldwide have used line structured light vision to accomplish 3D dimensional measurements of various targets in different scenes: bar section profile, railway tunnel clearance [8], crosssection profile [9], wheel geometric parameters [10], rail full profile [11], forgings size [12,13], weld contour [14] and the strip deviation in the finishing process [15]. Simultaneously, to achieve item size detection for large scenes, multicamera image acquisition and stitching detection schemes have been widely utilized [16].…”

Section: Introductionmentioning

confidence: 99%

Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Yan

et al. 2023

Meas. Sci. Technol.

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Front-end bending is one of the most significant defects of slabs in hot rough rolling. To address the lack of effective methods to detect slab front-end bending in hot rolling, a measurement system based on line structured light vision is proposed in this paper. Line structured light lasers and area-scan cameras are arranged on the supports on both the exit and entrance of a roughing mill. First, the calibration of the measurement system is completed by camera calibration and the random sampling consistency algorithm (RANSAC). Second, multiple region of interest (ROI) grey thresholds and the Steger algorithm are used to realize laser centreline extraction on the surface of a large-scale and high-temperature slab. Last, the actual value of the slab front-end bending are obtained and calculated through coordinate transformation. The proposed measurement system can detect slab front-end bending in real time and the detection error is less than 5 mm.

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Section: Introductionmentioning

confidence: 99%

Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Yan

et al. 2023

Meas. Sci. Technol.

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“…Gao et al [22] aimed at the rapid non-contact measurement problem of a revolving workpiece's radial and axial dimensions, a fast and high-precision visual inspection method has been presented in this paper. Yan et al [23] introduced a non-contact strip deviation online measurement method that relies on image detection and machine vision technology. This method offers the flexibility to adapt to varying strip heights, allowing for easy modifications.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms

Wang

Liu³

et al. 2023

Applied Sciences

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Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

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“…Recently, machine vision technology has advanced rapidly and has become a popular industrial measurement technique for its robustness and precision. In hot rolling mills, Yan 1 introduced a strip online deviation measurement system based on binocular vision, which could be well adapted to the change of the strip height; Carruthers-Watt 2 presented a visionbased system to measure the strip tracking, which used Bezier curves to reduce the impact of cooling water; Montague 3 measured the slab camber in hot strip rolling by machine vision online; Kong 4 introduced a camber measurement system based on two-area-scan cameras; Lin 5 introduced a machine vision-based method to recognize and classify the surface flaws of aluminum castings. However, in the actual production process, the strip runs at a high speed, which poses great challenges for reducing the latency of the measurement and automatic control of strip deviation.…”

Section: Introductionmentioning

confidence: 99%

A low-latency FPGA-based method for edge extraction of hot-rolled strip

Gao,

Li,

Cao

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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This paper proposes a low-latency method, based on FPGA, for edge extraction of hot-rolled strip. The method uses a global shutter camera module to capture strip images and detects the strip edge position by analyzing the intensity profile and the gradient of image gray values. The method has the advantages of low latency, strong robustness, and accurate results. The feasibility and effectiveness of the method are verified by FPGA simulation experiments, and a foundation is laid for the subsequent strip deviation automatic control algorithm.

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Online deviation measurement system of the strip in the finishing process based on machine vision

Cited by 7 publications

References 18 publications

Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms

A low-latency FPGA-based method for edge extraction of hot-rolled strip

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