Skip to main content
Log in

Automatic Detection of Slag Eye Area Based on a Hue-Saturation-Value Image Segmentation Algorithm

  • Applications of Autonomous Data Collection and Active Learning
  • Published:
JOM Aims and scope Submit manuscript

Abstract

Inert gas bubbling is widely applied in the ladle refining of molten steel to ensure homogeneity and to promote chemical reactions between the slag and the metal phases. Monitoring the slag behavior is important for controlling the steel quality. In the present study, an image segmentation algorithm based on the hue-saturation-value color space has been proposed to realize the automatic detection and calculation of the slag eye area. Laboratory experiments show that the developed method was stable and yielded accurate results, which could be applied to automatic industrial image detection. In addition, the proposed methodology is promising in other fields, such as the evaluation of particle diameter for the identification of aggregate size, detection of material cracks, and measurement of the lining thickness of a furnace.

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.

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

Similar content being viewed by others

References

  1. Q.N. Hoang, M.A. Ramírez-Argáez, A.N. Conejo, B. Blanpain, and A. Dutta, JOM 70, 2109 (2018)

    Article  Google Scholar 

  2. F. Tan, S. Jin, Z. He, and Y. Li, JOM 73, 2911 (2021)

    Article  Google Scholar 

  3. L. Li and B. Li, JOM 68, 2160 (2016)

    Article  Google Scholar 

  4. E.K. Ramasetti, V.V. Visuri, P. Sulasalmi, R. Mattila, and T. Fabritius, Steel Res. Int. 90, 1800365 (2019)

    Article  Google Scholar 

  5. F. Tan, Z. He, S. Jin, L. Pan, Y. Li, and B. Li, Steel Res. Int. 91, 1900606 (2020)

    Article  Google Scholar 

  6. F. Tan, S. Jin, Z. He, and Y. Li J. Iron Steel Res. Int. https://doi.org/10.1007/s42243-021-00647-6 (2021)

    Article  Google Scholar 

  7. Z. Liu, L. Li, and B. Li, ISIJ Int. 57, 1971 (2017)

    Article  Google Scholar 

  8. S. Chatterjee and K. Chattopadhyay, Metall. Mater. Trans. B 47, 508 (2016)

    Article  Google Scholar 

  9. L. Wu, P. Valentin, and D. Sichen, Steel Res. Int. 81, 508 (2010)

    Article  Google Scholar 

  10. W. Liu, H. Tang, S. Yang, M. Wang, J. Li, Q. Liu, and J. Liu, Metall. Mater. Trans. B 49, 2681 (2018)

    Article  Google Scholar 

  11. N. Lv, L. Wu, H. Wang, Y. Dong, and C. Su, J. Iron Steel Res. Int. 24, 243 (2017)

    Article  Google Scholar 

  12. E.S. Gadelmawla, Measurement 100, 36 (2017)

    Article  Google Scholar 

  13. Subagyo and G.A. Brooks, ISIJ Int. 43, 1286 (2003)

    Article  Google Scholar 

  14. K.J. Graham, K. Krishnapisharody, G.A. Irons, and J.F. MacGregor, Can. Metall. Q. 46, 397 (2007)

    Article  Google Scholar 

  15. X. Xu, G.A. Brooks, W. Yang, and S. Curic, Ironmak. Steelmak. 37, 620 (2010)

    Article  Google Scholar 

  16. X. Xu, G.A. Brooks, and W. Yang, Metall. Mater. Trans. B 41, 1025 (2010)

    Article  Google Scholar 

  17. B. Yu, EURASIP J. Image. Video Process. 2019, 1 (2019)

    Article  Google Scholar 

  18. N. Ostu, O. Nobuyuki, and N. Otsu, IEEE Trans. Syst. Man Cybern. 9, 62 (1979)

    Article  Google Scholar 

  19. A.P. Chakkaravarthy and A. Chandrasekar, 3D Res. 10, 1 (2019)

    Article  Google Scholar 

  20. A. Wunnava, M.K. Naik, R. Panda, B. Jena, and A. Abraham, Appl. Soft. Comput. 95, 106526 (2020)

    Article  Google Scholar 

  21. H. Jiang, L. Zeng, and B. Bi, Opt. Laser. Eng. 51, 34 (2013)

    Article  Google Scholar 

  22. J.P. Rodríguez, D.C. Corrales, J.-N. Aubertot, and C. Corrales, Pattern. Recognit. Lett. 136, 142 (2020)

    Article  Google Scholar 

  23. E. Hamuda, B. McGinley, M. Glavin, and E. Jones, Comput. Electron. Agric. 133, 97 (2017)

    Article  Google Scholar 

  24. V. Chernov, J. Alander, and V. Bochko, Comput. Electr. Eng. 46, 328 (2015)

    Article  Google Scholar 

  25. M.Á. Castillo-Martínez, F.J. Gallegos-Funes, B.E. Carvajal-Gámez, G. Urriolagoitia-Sosa, and A.J. Rosales-Silva, Comput. Electron. Agric. 178, 105783 (2020)

    Article  Google Scholar 

  26. T.F. Chan, S.H. Kang, and J. Shen, J. Vis. Commun. Image Represent. 12, 422 (2001)

    Article  Google Scholar 

  27. K.B. Shaik, P. Ganesan, V. Kalist, B.S. Sathish, and J.M.M. Jenitha, Procedia Comput. Sci. 57, 41 (2015)

    Article  Google Scholar 

  28. M.R. Olson, E. Graham, S. Hamad, P. Uchupalanun, N. Ramanathan, and J.J. Schauer, Sci Total. Environ. 548, 252 (2016)

    Article  Google Scholar 

  29. S.J.G. Shoba and A.B. Therese, Biomed. Signal Process. 62, 101986 (2020)

    Article  Google Scholar 

  30. M. Ezzahmouly, A. Elmoutaouakkil, M. Ed-Dhahraouy, H. Khallok, A. Elouahli, A. Mazurier, A. ElAlbani, and Z. Hatim, Heliyon 5, e02557 (2019)

    Article  Google Scholar 

  31. Z. Wang, Expert Syst. Appl. 145, 113102 (2020)

    Article  Google Scholar 

  32. T. Ellis, A. Abbood, and B. Brillault, Image Vis. Comput. 10, 136 (1992)

    Article  Google Scholar 

  33. A. Fitzgibbon, M. Pilu, and R.B. Fisher, IEEE. Trans. Pattern Anal. 21, 476 (1999)

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China [Grant Nos. 51974211, 12072245, and 51834002] and the Special Project of Central Government for Local Science and Technology Development of Hubei Province [Grant Nos. 2019ZYYD003, 2019ZYYD076].

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Fangguan Tan or Yawei Li.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there are no conflict of interest.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 214 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, G., Tan, F., Jin, S. et al. Automatic Detection of Slag Eye Area Based on a Hue-Saturation-Value Image Segmentation Algorithm. JOM 74, 2921–2929 (2022). https://doi.org/10.1007/s11837-021-05094-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-021-05094-y

Navigation