A Method for Measurement of Workpiece form Deviations Based on Machine Vision

Zhang, Wei; Han, Zongwang; Li, Yang; Cheng, Xiang

doi:10.3390/machines10080718

Cited by 8 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we used the optimal detection operator, i.e., Canny operator [22], and the detailed process of the algorithm is as follows: (1) filter and smooth grayscale images using a two-dimensional Gaussian filter [17]; (2) compute the gradient magnitude and direction using the Sobel operator; (3) apply maximum suppression to the image gradient magnitude; and (4) detect and connect edges using a double-thresholding algorithm. After edge detection by the Canny operator, as shown in Figure 2, the displacement is calculated by an ellipse-fitting algorithm with three concentric circles, that is, conducting least-squares fitting on the basis of edge detection, whose algorithm accuracy can reach sub-pixel level [23]. At present, the classical detection operators of edge-detection algorithms at whole-pixel level are the Roberts operator, Sobel operator, Canny operator, Laplace op ator, etc.…”

Section: Displacement Testing Principle Based On Edge Detectionmentioning

confidence: 99%

“…At present, the classical detection operators of edge-detection algorithms at whole-pixel level are the Roberts operator, Sobel operator, Canny operator, Laplace op ator, etc. In this paper, we used the optimal detection operator, i.e., Canny operator [2 and the detailed process of the algorithm is as follows: (1) filter and smooth graysc images using a two-dimensional Gaussian filter [17]; (2) compute the gradient magnitu and direction using the Sobel operator; (3) apply maximum suppression to the image g dient magnitude; and (4) detect and connect edges using a double-thresholding algorith After edge detection by the Canny operator, as shown in Figure 2, the displacemen calculated by an ellipse-fitting algorithm with three concentric circles, that is, conducti least-squares fitting on the basis of edge detection, whose algorithm accuracy can rea sub-pixel level [23].…”

Section: Displacement Testing Principle Based On Edge Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Tang

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

show abstract

Section: Displacement Testing Principle Based On Edge Detectionmentioning

confidence: 99%

Section: Displacement Testing Principle Based On Edge Detectionmentioning

confidence: 99%

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Tang

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Finally, the image edge contour was measured via geometric algorithm, and thus the bottom radius of the cylinder contour and the height of the column were obtained. Zhang et al [23] proposed a method for measuring the straightness, roundness and cylindricity of workpieces based on machine vision. A linear search algorithm and improved particle swarm optimization algorithm were proposed to evaluate the straightness and roundness deviation of workpieces.…”

Section: Introductionmentioning

confidence: 99%

Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision

Li,

Ge,

Shi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Aiming at addressing the problem of the online detection of automobile brake piston components, a non-contact measurement method based on the combination of machine vision and image processing technology is proposed. Firstly, an industrial camera is used to capture an image, and a series of image preprocessing algorithms is used to extract a clear contour of a test piece with a unit pixel width. Secondly, based on the structural characteristics of automobile brake piston components, the region of interest is extracted, and the test piece is segmented into spring region and cylinder region. Then, based on mathematical morphology techniques, the edges of the image are optimized. We extract geometric feature points by comparing the heights of adjacent pixel points on both sides of the pixel points, so as to calculate the variation of the spring axis relative to the reference axis (centerline of the cylinder). Then, we extract the maximum variation from all images, and calculate the coaxiality error value using this maximum variation. Finally, we validate the feasibility of the proposed method and the stability of extracting geometric feature points through experiments. The experiments demonstrate the feasibility of the method in engineering practice, with the stability in extracting geometric feature points reaching 99.25%. Additionally, this method offers a new approach and perspective for coaxiality measurement of stepped shaft parts.

show abstract

“…After image processing, this method uses improved Zernike moment subpixel edge detection algorithm to reposition the flange edge. Ruxin ZHEN et al [3] uses the saddle point method to locate the workpiece through binocular vision technology, and finally uses the plane vector to determine the size of the workpiece. In the image contour measurement method based on subpixel edge detection model proposed by Haoran RAN et al [4], a subpixel detection model is established by mathematical method to calculate the pixel circumference of the contour curve.…”

Section: Introductionmentioning

confidence: 99%

Optimization Scheme of Measuring Accuracy of Bearing Seat Size Based on Machine Vision

Wang,

Xu,

et al. 2024

IJCSIT

View full text Add to dashboard Cite

At present, the size measurement of industrial workpieces is mainly manual measurement and CMM measurement, which cannot meet the requirements of high precision, high efficiency and low error rate in industrial production at the same time. In order to solve the above problems, this paper uses monocular vision technology to propose an optimization scheme of workpiece size measurement accuracy based on machine vision, which takes the height and declination Angle between the camera lens and the workpiece as variables. Firstly, the image was captured by camera calibration correction, and morphological methods such as gray-scale, denoising, expansion and corrosion were used for image pre-processing. Secondly, features were extracted using double-threshold canny edge detection algorithm and connected domain algorithm. Finally, the bearing seat size was measured using the minimum external rectangle algorithm. The experiment shows that the higher the distance between the camera lens and the stage, the larger the error is when the image information can be collected completely. The greater the Angle between the center of the camera lens and the center of the workpiece, the greater the error; The device not only meets the precision requirements, but also has the advantages of fast response speed, which can meet the needs of industrial applications.

show abstract

A Method for Measurement of Workpiece form Deviations Based on Machine Vision

Cited by 8 publications

References 27 publications

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision

Optimization Scheme of Measuring Accuracy of Bearing Seat Size Based on Machine Vision

Contact Info

Product

Resources

About