Skip to main content
Log in

An intelligent vision system for detecting defects in glass products for packaging and domestic use

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Defect detection is an important task in glass manufacturing. Despite the importance of the visual inspection of glass products, many of the visual inspection processes are performed manually. The problem is that human inspection presents some drawbacks, such as being time-consuming, the high cost involved, and the lack of standardization. In this context, the development of automated processes for the inspection of glass products is important. In this paper, we propose an intelligent vision system for the automatic inspection of two types of defects in glass products: the first one is detection of a critical defect in glass cups for food packaging and domestic use, called glass sparkle or fragment of glass, and the second one is identification of a defect called deformation in plates for domestic use. To evaluate these applications, we used an apparatus consisting of a conveyor belt and a camera controlled by a PC to simulate an industrial line of production. The results indicate that the developed applications are suitable for the detection of investigated defects because for both applications, the hit rate was above 95 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Nishu G, Agrawal S (2011) Glass defect detection techniques using digital image processing—a review. Spec Issue IP Multimed Commun 1:65–67

    Google Scholar 

  2. Pesante-Santana JA, Woldstad JC (2000) The quality inspection task in modern manufacturing. In: Karwowski W (ed) Proc. of international encyclopedia of ergonomics. Taylor and Francis, London

    Google Scholar 

  3. Vernon D (1991) Machine vision: automated visual inspection and robot vision. Prentice Hall, Great Britain

    Google Scholar 

  4. Wang J, Asundi AKA (2000) Computer vision system for wineglass defect inspection via gabor-filter-based texture features. Inf Sci 127:157–171. doi:10.1016/S0020-0255(00)00036-0

    Article  Google Scholar 

  5. Batchelor BG, Whelan PF (2002) Intelligent vision systems for industry. Springer, London

    Google Scholar 

  6. Bulnes FG, Usamentiaga R, Garcia DF, Molleda J (2014) An efficient method for defect detection during the manufacturing of web materials. J Intell Manuf. doi:10.1007/s10845-014-0876-9

    Google Scholar 

  7. Kwak HJ, Park GT (2014) Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation. J Intell Manuf 25:303–318. doi:10.1007/s10845-012-0663-4

    Article  Google Scholar 

  8. Yepeng Z, Yuezhen T, Zhiyong F (2007) Application of digital image process technology to the mouth of beer bottle defect inspection. In: Proc. of 8th international conference on electronic measurement & instruments, Xian, China, 905–908

  9. Peng X, Chen Y, Yu W (2008) An online defects inspection method for float glass fabrication based on machine vision. Int J Adv Manuf Technol 39:1180–1189. doi:10.1007/s00170-007-1302-7

    Article  Google Scholar 

  10. Adamo F, Attivissimo F, Dinisio A, Savino M (2009) A low-cost inspection system for online defects assessment in satin glass. Measurement 42:1304–1311. doi:10.1016/j.measurement.2009.05.006

    Article  Google Scholar 

  11. Hassan MH, Diab S (2010) Visual inspection of products with geometrical quality characteristics of known tolerances. Ain Shams Eng J 1:79–84. doi:10.1016/j.asej.2010.09.011

    Article  Google Scholar 

  12. Zhao J, Zhao X, Liu YA (2011) Method for detection and classification of glass defects in low resolution images. In: Proc. of sixth international conference on image and graphics, Hefei, P.R.China, 642–647

  13. Pires AC, Santana JCC, Pessota JH, Araújo SA (2013) Implementation of a prototype for automatic beans selection. In: Proc. of XIX international conference on industrial engineering and operations management, Valladolid, 1–8

  14. Gonzalez RC, Woods RE (2002) Digital image processing. Addison-Wesley, Boston

    Google Scholar 

  15. Hough PVC (1962) Method and means for recognizing complex patterns. U.S. Patent 3069654

  16. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13:111–122. doi:10.1016/0031-3203(81)90009-1

    Article  MATH  Google Scholar 

  17. Davies ER (1986) Image space transform for detecting straight edges in industrial images. Pattern Recogn Lett 4:185–192. doi:10.1016/0167-8655(86)90018-8

    Article  Google Scholar 

  18. Brazilian Association of Technical Norms–ABNT (2002) NBR 14910:2002—glass packaging for food products

  19. Kim HY (2010) ProEikon—routines and programs in C/C++ for image processing and computer vision. http://www.lps.usp.br/~hae/software. Accessed 5 Mar 2010

  20. INTEL. OPENCV - Open Source Computer Vision Library. http://www.intel.com/technology/computing/opencv/. Accessed 5 Oct 2007

  21. Anami BS, Savakar DG (2010) Influence of light, distance and size on recognition and classification of food grains’ images. Int J Food Eng 6:1–21. doi:10.2202/1556-3758.1698

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sidnei Alves de Araújo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cabral, J.D.D., de Araújo, S.A. An intelligent vision system for detecting defects in glass products for packaging and domestic use. Int J Adv Manuf Technol 77, 485–494 (2015). https://doi.org/10.1007/s00170-014-6442-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6442-y

Keywords

Navigation