Automated visual classification of frequent defects in flat steel coils

  • 311 Accesses

  • 19 Citations


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.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.


  1. 1.

    Christmas I (2008) Steel’s climate change commitment. World Steel Association. Accessed 15 July 2010

  2. 2.

    Gonvarri Group (2009) Processes. Accessed 30 October 2009

  3. 3.

    Stahleisen (1996) Surface defects in hot rolled flat steel products. Stahleisen, Duesseldorf

  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. 723–726

  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. 742–746

  6. 6.

    Piuri V, Scotti F, Roveri M (2005) Computational intelligence in industrial quality control. IEEE International Workshop on Intelligent Signal Processing, pp. 4–9

  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.

  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):200–209

  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. 239–242

  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. 5034–5037

  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. 125–127

  12. 12.

    ISRA VISION Parsytec (2009) Accessed 15 May 2009

  13. 13.

    Davies ER (2004) Machine vision: theory, algorithms, practicalities. Kaufmann, San Francisco, pp 103–130

  14. 14.

    Rodenacker K, Bengtsson E (2003) A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 25:1–36

  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):269–285

  16. 16.

    Sobral JL (2005) Leather inspection based on wavelets. Pattern Recognit Image Anal 3523:682–688

  17. 17.

    Gabor D (1946) Theory of communication. J Inst Elect Eng 93(III):429–457

  18. 18.

    Kamarainen JK (2003) Feature extraction using Gabor filters. PhD thesis, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland, pp. 17–56

  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):691–712

  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):1160–1169

  21. 21.

    Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16(7):474–482

  22. 22.

    Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971

  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. 805–810

  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. 1014–1018

  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 343–385

  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. 1580–1584

  27. 27.

    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

  28. 28.

    Turner MR (1986) Texture discrimination by Gabor functions. Biol Cybern 55(2):71–82

  29. 29.

    Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell 12(1):55–73

  30. 30.

    Bianconi F, Fernández A (2007) Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognit 40(12):3325–3335

  31. 31.

    Clausi DA, Jernigan ME (2000) Designing Gabor filters for optimal texture separability. Pattern Recognit 33(11):1835–1849

  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. 809–814

  33. 33.

    Mak KL, Peng P, Yiu KFC (2009) Fabric defect detection using morphological filters. Image Vis Comput 27(10):1585–1592

  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. 95–100

  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 28:229–289

  36. 36.

    Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructures of cognition, Vol. 1. MIT, Cambridge, pp 62–318

  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.

  38. 38.

    Berg R (2005) Sensitivity and specificity. Clin Med Res 3:56

Download references

Author information

Correspondence to Roberto Medina.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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

Video 1

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

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Medina, R., Gayubo, F., González-Rodrigo, L.M. et al. Automated visual classification of frequent defects in flat steel coils. Int J Adv Manuf Technol 57, 1087–1097 (2011).

Download citation


  • Feature extraction
  • Machine vision
  • Image processing
  • Image classification