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Automated visual classification of frequent defects in flat steel coils

  • Roberto Medina
  • Fernando Gayubo
  • Luis M. González-Rodrigo
  • David Olmedo
  • Jaime Gómez-García-Bermejo
  • Eduardo Zalama
  • José R. Perán
ORIGINAL ARTICLE

Abstract

Surface inspection is one of the most important facets of quality-control systems in the steel manufacturing and processing industry. A number of surface defects can be detected by a visual inspection. However, human visual inspection becomes a hard task due to the high-processing speed. In the present paper, the automated visual inspection of flat steel is approached. A detailed description is given of the main aspects involved, concerning image acquisition, image processing algorithms, architecture design, the custom software developed, and data transmission and synchronization. Particular attention is paid to feature extraction and classification. Six kinds of defects are finely classified: weld, white rust, transporter marks, pitting corrosion, protuberance of zinc, and rolled marks. The proposed solution has been implemented and tested in a real industrial environment, in a flat steel cutting factory, showing suitable results. A successful classification ratio of about 87% of the known defects has been obtained, which is considered a reliable result.

Keywords

Feature extraction Machine vision Image processing Image classification 

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Supplementary material

Video 1

Automated visual inspection system installed by CARTIF in a real cutting steel factory (MPG 12108 kb)

References

  1. 1.
    Christmas I (2008) Steel’s climate change commitment. World Steel Association. http://www.worldsteel.org/?action=storypages&id=294. Accessed 15 July 2010
  2. 2.
    Gonvarri Group (2009) Processes. http://www.gonvarri.com/en/procesos/procesos.asp. Accessed 30 October 2009
  3. 3.
    Stahleisen (1996) Surface defects in hot rolled flat steel products. Stahleisen, DuesseldorfGoogle Scholar
  4. 4.
    Gayubo F, González JL, de la Fuente E, Miguel F, Perán JR (2006) On-line machine vision system for detect split defects in sheet-metal forming processes. Conference on Pattern Recognition (ICPR 2006), Vol. 1, pp. 723–726Google Scholar
  5. 5.
    Nishibe K, Fujiwara N (1999) Development of automatic steel coil recognition system for automated crane. Proceedings of the 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 742–746Google Scholar
  6. 6.
    Piuri V, Scotti F, Roveri M (2005) Computational intelligence in industrial quality control. IEEE International Workshop on Intelligent Signal Processing, pp. 4–9Google Scholar
  7. 7.
    Tomczac L, Mosorov V, Sankowski D, Nowakowski J (2007) Image defect detection methods for visual inspection systems. 9th International Conference—The Experience of Designing and Applications of CAD Systems in Microelectronics (CADSM’07), pp. 454–456.Google Scholar
  8. 8.
    Acciani G, Brunetti G, Fornarelli G (2006) Application of neural networks in optical inspection and classification of solder joints in surface mount technology. IEEE Trans Ind Inf 2(3):200–209CrossRefGoogle Scholar
  9. 9.
    Jia H, Murphey YL, Shi J, Chang TS (2004) An intelligent real-time vision system for surface defect detection. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Vol. 3, pp. 239–242Google Scholar
  10. 10.
    Kang GW, Liu HB (2005) Surface defects inspection of cold rolled strips based on neural network. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vol. 8, pp. 5034–5037Google Scholar
  11. 11.
    Sharifzadeh M, Alirezaee S, Amirfattahi R, Sadri S (2008) Detection of steel defect using the image processing algorithms. IEEE International Multitopic Conference, pp. 125–127Google Scholar
  12. 12.
    ISRA VISION Parsytec (2009) http://www.parsytec.de/. Accessed 15 May 2009
  13. 13.
    Davies ER (2004) Machine vision: theory, algorithms, practicalities. Kaufmann, San Francisco, pp 103–130Google Scholar
  14. 14.
    Rodenacker K, Bengtsson E (2003) A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 25:1–36Google Scholar
  15. 15.
    Weska JS, Charles RD, Rozenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Systems, Man, and Cybernetics 6(4):269–285Google Scholar
  16. 16.
    Sobral JL (2005) Leather inspection based on wavelets. Pattern Recognit Image Anal 3523:682–688CrossRefGoogle Scholar
  17. 17.
    Gabor D (1946) Theory of communication. J Inst Elect Eng 93(III):429–457Google Scholar
  18. 18.
    Kamarainen JK (2003) Feature extraction using Gabor filters. PhD thesis, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland, pp. 17–56Google Scholar
  19. 19.
    Bovik AC, Gopal N, Emmoth T, Restrepo A (1992) Localized measurement of emergent image frequencies by Gabor wavelets. IEEE Trans Inf Theory 38(2):691–712CrossRefGoogle Scholar
  20. 20.
    Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A 2(7):1160–1169CrossRefGoogle Scholar
  21. 21.
    Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16(7):474–482CrossRefGoogle Scholar
  22. 22.
    Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971CrossRefGoogle Scholar
  23. 23.
    Havlicek JP, Bovik AC, Maragos P (1992) Modulation models for image processing and wavelet-based image demodulation. 1992 Conference Record of Twenty-Sixth Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 805–810Google Scholar
  24. 24.
    Havlicek JP, Harding DS, Bovik AC (1996) Extracting essential modulated image structure. Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1014–1018Google Scholar
  25. 25.
    Havlicek JP, Bovik AC, Chen D (1999) AM-FM image modeling and Gabor analysis. In: Chen CW, Zhang Y (eds) In visual information representation, communication, and image processing. Marcel Dekker, New York, pp 343–385Google Scholar
  26. 26.
    Havlicek JP, Tang J, Acton ST, Antonucci R, Ouandji FN (2003) Modulation domain texture retrieval for CBIR in digital libraries. Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1580–1584Google Scholar
  27. 27.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842CrossRefGoogle Scholar
  28. 28.
    Turner MR (1986) Texture discrimination by Gabor functions. Biol Cybern 55(2):71–82Google Scholar
  29. 29.
    Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell 12(1):55–73CrossRefGoogle Scholar
  30. 30.
    Bianconi F, Fernández A (2007) Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognit 40(12):3325–3335zbMATHCrossRefGoogle Scholar
  31. 31.
    Clausi DA, Jernigan ME (2000) Designing Gabor filters for optimal texture separability. Pattern Recognit 33(11):1835–1849CrossRefGoogle Scholar
  32. 32.
    Kong Z, Cai Z (2007) Advances of research in fuzzy integral for classifiers’ fusion. Proceedings of the 8th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing, Vol. 2, pp. 809–814Google Scholar
  33. 33.
    Mak KL, Peng P, Yiu KFC (2009) Fabric defect detection using morphological filters. Image Vis Comput 27(10):1585–1592CrossRefGoogle Scholar
  34. 34.
    Amaral AL, Alves MM, Mota M, Ferreira EC (1997) Morphological characterisation of microbial aggregates by image analysis. Proceedings of the 9th Portuguese Conference on Pattern Recognition (RecPad’97), pp. 95–100Google Scholar
  35. 35.
    Hubel DH, Wiesel TN (1965) Receptive fields and functional architecture in two nonstriate visual areas visual areas 18 and 19 of the cat. J Neurophysiol 28:229–289Google Scholar
  36. 36.
    Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructures of cognition, Vol. 1. MIT, Cambridge, pp 62–318Google Scholar
  37. 37.
    Berchtold S, Keim DA, Kriegel HP (1996) The X-tree: an index structure for high-dimensional data. Proceedings of 22nd International Conference on Very Large Data Bases, pp. 28–39.Google Scholar
  38. 38.
    Berg R (2005) Sensitivity and specificity. Clin Med Res 3:56CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Roberto Medina
    • 1
  • Fernando Gayubo
    • 1
  • Luis M. González-Rodrigo
    • 1
  • David Olmedo
    • 1
  • Jaime Gómez-García-Bermejo
    • 2
  • Eduardo Zalama
    • 2
  • José R. Perán
    • 2
  1. 1.CARTIF FoundationBoecilloSpain
  2. 2.Industrial Engineering SchoolUniversity of ValladolidValladolidSpain

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