Skip to main content
SpringerLink
Log in

Steel Strip Surface Defect Identification using Multiresolution Binarized Image Features

  • Technical Article---Peer-Reviewed
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

The shaped steel strip, in the hot rolling process, may exhibit some surface flaws. Their origin could be the internal discontinuities in the input product or the thermomechanical transformation of the material, during the shaping process. Such defects are of a random occurrence and may lead to costly rework operations or to a downgrading of the final product. So, they should be detected and identified as soon as possible, to allow a timely decision-making. For such a quality monitoring, the used vision systems are mainly based on an image description and a reliable classification. In this paper, we explore pre-defined image filters and work on a procedure to extract a discriminant image feature, while realizing the best trade-off between the improved recognition rate of the surface defects and the computing time. The proposed method is a multiresolution approach, based on the Binarized Statistical Image Features method, employed to date in biometrics. The filters, pre-learnt from natural images, are applied to steel defect images as a new surface structure indicator. They provide a quite discriminating image description. A relevant data reduction is used together with a classifier to allow an efficient recognition rate of the defective hot rolled products.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. F. Dupont, C. Odet, M. Carton, Optimization of the Recognition of Defects in Flat Steel Products with the Cost Matrices Theory. NDT&E Int. 30(1), 3–10 (1997)

    Article  Google Scholar 

  2. N. Neogi, D.K. Mohanta, P.K. Dutta, Review of Vision-Based Steel Surface Inspection Systems. EURASIP J. Image Video Process. 1, 50 (2014)

    Article  Google Scholar 

  3. J.H. Guo, X.D. Meng, M.D. Xiong, Study on Defection Segmentation for Steel Surface Image Based on Image Edge Detection and Fisher Discriminant. J. Phys. Conf. Ser. 48, 364–368 (2006)

    Article  Google Scholar 

  4. W. Xiu-yong, X. Ke, X. Jin-wu, Application of Undecimated Wavelet Transform to Surface Defect Detection of Hot Rolled Steel Plates, in IEEE Congress on Image and, Signal Processing (2008), pp. 528–532

  5. S. Ghorai, A. Mukherjee, M. Gangadaran, P.K. Dutta, Automatic Defect Detection on Hot-Rolled Flat Steel Products. IEEE Trans. Instrum. Meas. 62(3), 612–621 (2013)

    Article  Google Scholar 

  6. Y.J. Jeon, D.C. Choi, J.P. Yun, C. Park, H. Bae, S.W. Kim, Automated Inspection Algorithm for Thick Plate Using Dual Light Switching Lighting Method. Int. J. Electr. Comput. Eng. 6(12), 1475–1478 (2012)

    Google Scholar 

  7. C. Tikhe, J.S. Chitode, Metal Surface Inspection for Defect Detection and Classification using Gabor Filter. Int. J. Innov. Res. Sci. Eng. Technol. 3(6), 13702 (2014)

    Google Scholar 

  8. D.C. Choi, Y.J. Jeon, S.J. Lee, J.P. Yun, S.W. Kim, Oilmarks Detection Algorithm in Steel Plates, Latest Trends on Systems, in Proceedings of the 18th International Conference on Systems (part of CSCC '14), vol 1 (2014), pp. 239–242

  9. G. Wu, H. Kwak, S. Jang, K. Xu, J. Xu, Design of Online Surface Inspection System of Hot Rolled strips, in Proceedings of the IEEE International Conference on Automation and Logistics (Qingdao, China, 2008), pp. 2291–2295

  10. F.G. Bulnes, R. Usamentiaga, DF Garc’ia, J Molleda, Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials. Sensors 12, 10788–10809 (2012). https://doi.org/10.3390/s120810788

    Article  Google Scholar 

  11. D. Djukic, S. Spuzic, Statistical Discriminator of Surface Defects on Hot Rolled Steel, in Proceedings of Image and Vision Computing (University of Waikato, Hamilton, New Zealand, 2007), pp. 158–163

  12. X. Xie, A Review of Recent Advances in Surface Defect Detection Using Texture Analysis Techniques. Electron. Lett. Comput. Vis. Image Anal. 7(3), 1–22 (2008)

    CAS  Google Scholar 

  13. M. Chu, R. Gong, Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect. ISIJ Int. 55(9), 1956–1962 (2015)

    Article  CAS  Google Scholar 

  14. K. Song, Y. Yan, A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects. Appl. Surf. Sci. 285, 858–864 (2013)

    Article  CAS  Google Scholar 

  15. T. Maenpaa, Surface Quality Assessment with Advanced Texture Analysis Techniques (Proceedings of International Surface Inspection Summit, Luxembourg, 2006)

    Google Scholar 

  16. M. Xiao, M. Jiang, G. Li, Li Xie, Li Yi, An Evolutionary Classifier for Steel Surface Defects with Small Sample Set. EURASIP J. Image Video Processing (2017). https://doi.org/10.1186/s13640-017-0197-y

    Article  Google Scholar 

  17. J. Kannala, E. Rahtu, BSIF: Binarized Statistical Image Features, in Proceedings of 21st International Conference on Pattern Recognition (ICPR 2012) (Tsukuba, Japan, 2012), pp. 1363–1366.

  18. J. Ylioinas, J. Kannala, A. Hadid, M. Pietikäinen, Face Recognition Using Smoothed High-Dimensional Representation, in Image Analysis, SCIA. Lecture Notes in Computer Science, vol 9127, ed. by R. Paulsen, K. Pedersen (Springer, Cham, 2015)

  19. R.A. Shervin, Multiscale Binarised Statistical Image Features for Symmetric Face Matching Using Multiple Descriptor Fusion Based on Class-Specific LDA (Springer, London, 2015)

    Google Scholar 

  20. A. Hyvarinen, J. Hurri, P.O. Hoyer, Natural Image Statistics. A Probabilistic Approach to Early Computational Vision (Springer, Berlin, 2009)

    Google Scholar 

  21. T. Ojala, M. Pietikäinen, T. Maenpaa, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  22. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  23. A.M. Martinez, A.C. Kak, PCA Versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)

    Article  Google Scholar 

  24. P.H. Bugatti, A.J.M. Traina, J.C. Felipe, C. Traina, A New Method to Efficiently Reduce Histogram Dimensionality, in Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915, ed. by M.L. Giger, N. Karssemeijer (2008), pp. 1605–7422. https://doi.org/https://doi.org/10.1117/12.770512

  25. Y. Jeon, D.-C. Choi, J.P. Yun, C. Park, S.W. Kim. Detection of Scratch Defects on Slab Surface, in 1th International Conference on Control, Automation and Systems, 2011, pp. 1274–1278

  26. K. Song, S. Hu, Y. Yan, Automatic Recognition of Surface Defects on Hot-rolled Steel Strip using Scattering Convolution Network. J. Comput. Inf. Syst. 10(7), 3049–3055 (2014). https://doi.org/10.12733/jcis10026

    Article  Google Scholar 

  27. L. Yi, G. Li, M. Jiang, An End-to-End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks. Int Steel Res (2016). https://doi.org/10.1002/srin.201600068

    Article  Google Scholar 

  28. M.W. Ashour, F. Khalid, A.A. Halin, L.N. Abdullah, S.H. Darwish, Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features. J. Sci. Eng. Arab. (2018). https://doi.org/10.1007/s13369-018-3329-5

    Article  Google Scholar 

  29. F. Zhou, G. Liu, F. Xu, H. Deng, A Generic Automated Surface Defect Detection Based on a Bilinear Model. Appl. Sci. 9, 3159 (2019). https://doi.org/10.3390/app9153159

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Centre in Industrial Technologies CRTI, P.O. Box 64, 16014, Chéraga, Algiers, Algeria

    Zoheir Mentouri

  2. Laboratory of Electrical Engineering-LGEG, Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria

    Abdelkrim Moussaoui

  3. Laboratory of Advanced Control-LABCAV, Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria

    Zoheir Mentouri & Djalil Boudjehem

  4. Laboratory of Inverse Problems-PI:MIS, Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria

    Hakim Doghmane

Authors
  1. Zoheir Mentouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelkrim Moussaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Djalil Boudjehem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hakim Doghmane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zoheir Mentouri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mentouri, Z., Moussaoui, A., Boudjehem, D. et al. Steel Strip Surface Defect Identification using Multiresolution Binarized Image Features. J Fail. Anal. and Preven. 20, 1917–1927 (2020). https://doi.org/10.1007/s11668-020-01012-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-020-01012-7

Keywords

Search

Navigation