Roundness measurement of cigarette based on visual information

Jun-Li Cao , Ju-Feng Li , Teng-Da Lu

Advances in Manufacturing ›› 2017, Vol. 5 ›› Issue (2) : 177 -181.

PDF
Advances in Manufacturing ›› 2017, Vol. 5 ›› Issue (2) : 177 -181. DOI: 10.1007/s40436-017-0176-7
Article

Roundness measurement of cigarette based on visual information

Author information +
History +
PDF

Abstract

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.

Keywords

Roundness measurement / Ellipse fitting / Image processing / Visual detection system

Cite this article

Download citation ▾
Jun-Li Cao, Ju-Feng Li, Teng-Da Lu. Roundness measurement of cigarette based on visual information. Advances in Manufacturing, 2017, 5(2): 177-181 DOI:10.1007/s40436-017-0176-7

登录浏览全文

4963

注册一个新账户 忘记密码

References

[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. Automated ellipse detection and location method on 3D visual inspection. Comput Eng Appl, 2011, 47(17): 169-171.

[4]

Zhang YJ. Image processing and analysis, 1999, Beijing: Tsinghua University Press

[5]

Yan B, Wang B, Li Y. Optimal ellipse fitting method based on least square. J Beijing Univ Aeronaut Astronaut, 2008, 34(3): 295-298.

[6]

Zhang WG, Han J, Zhou X. Data registration method for multi resolution measurement system with line structured light. Chin J Sci Instrum, 2013, 7: 2-8.

[7]

Bradski GR, Kaehler A. Learning OpenCV: computer vision with the OpenCV library, 2008, California: O’Reilly Media

[8]

Li L, He MY, Li N. Camera calibration based on the circular pattern and ellipse fitting. J Xidian Univ, 2010, 6: 1148-1154.

[9]

Zou YM, Wang B. Fragmental ellipse fitting based on least square algorithm. Chin J Sci Instrum, 2006, 27(7): 808-812.

[10]

Kanatani K, Sugaya Y, Kanazawa Y. Ellipse fitting for computer vision: implementation and applications, 2016, Williston: Morgan & Claypool

[11]

Fitzgibbon A, Pilu M, Fisher RB. Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell, 1999, 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

AI Summary AI Mindmap
PDF

155

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/