Roundness measurement of cigarette based on visual information
Received date: 2016-05-19
Revised date: 2017-04-24
Online published: 2017-06-25
Supported by
This project was supported by the Changde Cigarette Factory, Hunan Province. The authors express their sincere appreciation to the anonymous referees for their helpful comments to improve the quality of the paper
Roundness is defined as the degree that the cross section of an object is close to a theoretical circle. In the cigarette production process, the quality and production efficiency of a cigarette are directly affected by the roundness of the un-cut cigarette. To improve the current measurement method using a charge-coupled device (CCD) sensor and measure the roundness of cigarettes in the production line, a visual detection system composed of an industrial camera and a structural light is developed. The system's roundness-calculation method is closer to the real environment of the cigarette roundness. In this visual system, the line-structure light shines on the cigarette with a fixed angle and height in a longitudinal section, forming a crescent-shaped spot when the industrial camera cannot capture the cigarette's end surface. Then, the spot is analyzed using image-processing techniques, such as a median filter and ellipse fitting, after the industrial camera captures the spot. The system with a non-contact measurement style can meet the requirements of on-line cigarette detection with stable results and high precision.
Jun-Li Cao , Ju-Feng Li , Teng-Da Lu . Roundness measurement of cigarette based on visual information[J]. Advances in Manufacturing, 2017 , 5(2) : 177 -181 . DOI: 10.1007/s40436-017-0176-7
1. Wang JN (2011) Research on reform and development of tobacco industry in China. Dissertation, University of Jilin
2. Fen H, Li JF, Zhao JQ (2015) The measurement system of the cigarette surface quality. Dissertation, Shanghai University
3. Han JD, Yang HJ, Lu NG (2011) Automated ellipse detection and location method on 3D visual inspection. Comput Eng Appl 47(17):169-171
4. Zhang YJ (1999) Image processing and analysis. Tsinghua University Press, Beijing
5. Yan B, Wang B, Li Y (2008) Optimal ellipse fitting method based on least square. J Beijing Univ Aeronaut Astronaut 34(3):295-298
6. Zhang WG, Han J, Zhou X (2013) Data registration method for multi resolution measurement system with line structured light. Chin J Sci Instrum 7:2-8
7. Bradski GR, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. O'Reilly Media, California
8. Li L, He MY, Li N (2010) Camera calibration based on the circular pattern and ellipse fitting. J Xidian Univ 6:1148-1154
9. Zou YM, Wang B (2006) Fragmental ellipse fitting based on least square algorithm. Chin J Sci Instrum 27(7):808-812
10. Kanatani K, Sugaya Y, Kanazawa Y (2016) Ellipse fitting for computer vision: implementation and applications. Morgan & Claypool, Williston
11. Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476-480
12. Xia J (2007) Research on ellipse fitting method. Dissertation, Jinan University
13. Xu JZ (2012) Research on methods and evaluation of stripe center extraction in structured light 3D measurement. Dissertation, Nanjing University
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