Research on Image Measurement Method of Flat Parts Based on the Adaptive Chord Inclination Angle Algorithm
Source abstract
Abstract
To accurately measure the critical dimensions of flat parts using machine vision, an inspection method based on the adaptive chord inclination angle progressively screening the segmentation points of graphic elements was proposed in this study. The method doubled the size of the part image using bicubic interpolation, extracted the single-pixel contour with more detailed information, and designed an adaptive step size to obtain the front and back chord inclination angles of the contour. The method of complementing the front and back chord inclination angles was employed to avoid the negative effects of contour jaggedness, thereby obtaining the contour segmentation points after the initial screening. The segmentation points obtained in the initial sieving were divided into different point clusters according to the distance, and the contour, which was segmented by two segmentation points in different point clusters, was fitted using the least squares. The fitting results were evaluated, and all the fitting results were selected using the improved non-maximum suppression (NMS) algorithm to obtain the precisely selected segmentation points of the graphic elements. Consequently, the segmented individual graphic elements were fitted with the segmentation points as constraints to obtain the key dimensions of the closed part. The developed method could accurately find the contour segmentation points, and the relative error was less than 0.6%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image and Object Detection Techniques · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Funding
Research Program of science and technology at Universities of Inner Mongolia Autonomous Region
References (31)
Show source-provided references
1. A Machine Vision Algorithm for Quality Control Inspection of Gears10.1007/s00170-019-04426-2 ↗
2. Laplacian Operator-Based Edge Detectors10.1109/TPAMI.2007.1027 ↗
3. A Computational Approach to Edge Detection10.1109/TPAMI.1986.4767851 ↗
4. Nixon, M.S., and Aguado, A.S. (2012). Feature Extraction & Image Processing for Computer Vision, Academic Press.
5. Perona, P., and Malik, J. (1990, January 4–7). Detecting and Localizing Edges Composed of Steps, Peaks and Roofs. Proceedings of the Third International Conference on Computer Vision, Osaka, Japan.
6. Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues10.1109/TPAMI.2004.1273918 ↗
7. Xiaofeng, R., and Bo, L. (2012). Advances in Neural Information Processing Systems, Curran Associates, Inc.
8. Holistically-Nested Edge Detection10.1007/s11263-017-1004-z ↗
9. Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2015, January 9–12). Deeply-Supervised Nets. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, San Diego, CA, USA.
10. Measurement Algorithm of Notch Length of Plastic Parts Based on Video10.1088/1361-6501/ac61c3 ↗
11. Topological Structural Analysis of Digitized Binary Images by Border Following10.1016/0734-189X(85)90016-7 ↗
12. Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique10.1109/TMM.2008.2001384 ↗
13. Segmentation and Recognition Techniques for Planar Contour
14. Contour Feature Point Detection Algorithm of CCD Image
15. Mo, Q. (2011). Research on Machine Vision Measurement in Geometry of Sheet Metal Parts. [Master’s Thesis, Huazhong University of Science & Technology].
16. Wu, J. (2008). Research on Key Technologies in Dimensional Inspection System of Thin Sheet Parts Based on Machine Vision. [Ph.D. Thesis, Huazhong University of Science & Technology].
17. Zhou, C. (2016). Research of Parts Size Measurement System Based on Vision Technology. [Master’s Thesis, Zhejiang Sci-Tech University].
18. Recognition of machining position of mechanical parts based on shape and position feature
19. Rachmawati, E., Supriana, I., and Khodra, M.L. (2017, January 25–26). FAST Corner Detection in Polygonal Approximation of Shape. Proceedings of the 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, Indonesia.10.1109/ICSITech.2017.8257104 ↗
20. Liu, Y. (2013). Research on feature and constraint based visual reverse technology for two dimensional parts. [Master’s Thesis, Harbin Institute of Technology].
21. Wang, Y. (2013). Research of reverse technology for accurate parts based on two dimensional computer vision. [Master’s Thesis, Harbin Institute of Technology].
22. A new approach to extract feature points on the boundary
23. Representation method of outlines by using critical points
24. Petkov, N., and Westenberg, M.A. A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves. Proceedings of the Computer Analysis es and Patterns.
25. Corner Detection Algorithm Based on Image Contour Curve
26. Vision Measurement for Flat Parts Based on Local Line-Angle Contour Segmentation10.1088/1361-6501/ac6c77 ↗
27. Li, Y., Du, W., Wang, J., Duan, N., Guan, B., Tang, H., and Li, B. (2016). Research on the method of planar contour segmentation and recognition of the geometric primitives. Manuf. Technol. Mach. Tool, 85–88.
28. Wang, Y., and Wu, W. (2007). Accurate Reconstruction for Planar Contour Based on Segmentation and Recognition Techniques. J. Eng. Graph., 43–48.
29. Data Analysis and Regression10.1080/00224065.1979.11980888 ↗
30. Improved Algorithm for Minimum Zone of Roundness Error Evaluation Using Alternating Exchange Approach10.1088/1361-6501/ac40a6 ↗
31. Image corner detection method based on multi-scale discrete curvature