Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Glass Defect Detection Techniques using Digital Image Processing –A Review

Print
PDF
IP Multimedia Communications
© 2011 by IJCA Journal
ISBN : 978-93-80864-99-3
Year of Publication: 2011
Authors:
Nishu
Sunil Agrawal

Nishu and Sunil Agrawal. Glass Defect Detection Techniques using Digital Image Processing A Review. Special issues on IP Multimedia Communications (1):65-67, October 2011. Full text available. BibTeX

@article{key:article,
	author = {Nishu and Sunil Agrawal},
	title = {Glass Defect Detection Techniques using Digital Image Processing A Review},
	journal = {Special issues on IP Multimedia Communications},
	month = {October},
	year = {2011},
	number = {1},
	pages = {65-67},
	note = {Full text available}
}

Abstract

Glass defects are a major reason for poor quality and of embarrassment for manufacturers. It is a tedious process to manually inspect very large size glasses. The manual inspection process is slow, time-consuming and prone to human error. Automatic inspection systems using image processing can overcome many of these disadvantages and offer manufacturers an opportunity to significantly improve quality and reduce costs. In this paper we review various glass defects and the possible automated solutions using image processing techniques for defect detection.

Reference

  1. Jie Zhao, Xu Zhao and Yuncai Liu, “A Method for Detection and Classification of Glass Defects in Low Resolution Images,” Sixth International Conference on Image and Graphics, 2011, pp.642-647.
  2. Francesco Adamo and Mario Savino, “A low-cost Inspection system for online defects assessment in satin glass,” 2009, pp. 1304-1311.
  3. F. Adamo, F. Attivissimo, A. Di Nisio, M. Savino, An Automated visual inspection system for the glass industry,In: Proc. of 16th IMEKO TC4 Symposium, Florence, Italy, Sept. 22–24, 2008.
  4. Peng X, Chen Y and Yu W, “An online defects inspection method for float glass fabrication based on machine vision,” International Journal of Advanced Manufacturing Technology, vol.39, 2008, pp.1180-1189.
  5. Zhang Yepeng, Tao Yuezhen, Fan Zhiyong,” Application of Digital Image Process Technology to the Mouth of Beer Bottle Defect Inspection,” 2007, pp. 2-905- 2-908.
  6. Chang-Hwan Oh, Hyonam Joo and Keun-Ho Rew,” Detecting Low-Contrast Defect Regions on Glasses Using Highly Robust Model-Fitting Estimator,” International Conference on Control, Automation and Systems, 2007, pp. 2138 - 2141.
  7. K.H. Rew, T.H. Nam, H. Joo, and K.W. KO, “Enhancement of Illumination Irregularity for the 2D Blot Detection under Low Contrast,” Journal of the Korean Society for Precision Engineering, Vol. 24, 2007, No. pp. 29- 35.
  8. J.Y. Lee, and S.I. Yoo, “Automatic Detection of Region- Mura Defect in TFT-LCD,” IEICE TRANS. INF. & SYST., Vol. E87-D, No. 10, 2004, pp. 2371-2378.
  9. E.N. Malamas, E.G.M. Petrakis, M. Zervakis, L. Petit, J.D. Legat, A survey on industrial vision systems,applications and tools, Image and Vision Computing 21, 2003, 171–188.
  10. Makoto Shimizu, Akira Ishii and Toshio Nishimura, “Detection of Foreign Material Included in LCD Panels,” IEEE conf 2000, pp.836-841
  11. M.Leconte, Laser glass inspection system, International Society for Optical Engineering, 1997, 878–882.
  12. B.G. Batchelor and P. F. Whelan, "Intelligent Vision Systems for Industry,” Springer-Verlag, London, 1997, pp. 360.
  13. M.A.Coulthard, “Image Processing for Automatic Surface Defect Detection,” Surface Inspection Ltd, UK, pp. 192-196.
  14. R.Browing.“Recent Advances in Automated Patterned Wafer Inspection,”Proc. of SPIE, Vol. 1087, 1989, pp.440 – 445.