OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 22 — Aug. 1, 2014
  • pp: 4865–4872

Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method

Doo-Chul Choi, Yong-Ju Jeon, Sang Jun Lee, Jong Pil Yun, and Sang Woo Kim  »View Author Affiliations


Applied Optics, Vol. 53, Issue 22, pp. 4865-4872 (2014)
http://dx.doi.org/10.1364/AO.53.004865


View Full Text Article

Enhanced HTML    Acrobat PDF (861 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Presently, product inspection based on vision systems is an important part of the steel-manufacturing industry. In this work, we focus on the detection of seam cracks in the edge region of steel plates. Seam cracks are generated in the vertical direction, and their width range is 0.2–0.6 mm. Moreover, the gray values of seam cracks are only 20–30 gray levels lower than those of the neighboring surface. Owing to these characteristics, we propose a new algorithm for detecting seam cracks using a Gabor filter combination method. To enhance the performance, we extracted features of seam cracks and employed a support vector machine classifier. The experimental results show that the proposed algorithm is suitable for detecting seam cracks.

© 2014 Optical Society of America

OCIS Codes
(150.3040) Machine vision : Industrial inspection
(150.1135) Machine vision : Algorithms

ToC Category:
Machine Vision

History
Original Manuscript: August 7, 2013
Revised Manuscript: June 13, 2014
Manuscript Accepted: June 14, 2014
Published: July 22, 2014

Citation
Doo-Chul Choi, Yong-Ju Jeon, Sang Jun Lee, Jong Pil Yun, and Sang Woo Kim, "Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method," Appl. Opt. 53, 4865-4872 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-22-4865


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. Y. H. Tseng and D. M. Tsai, “Defect detection of uneven brightness in low-contrast images using basis image representation,” Pattern Recogn. 43, 1129–1141 (2010). [CrossRef]
  2. J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 91, 1400–1407 (2008).
  3. C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005). [CrossRef]
  4. C. Chan and G. K. H. Pang, “Fabric defect detection by Fourier analysis,” IEEE Trans. Ind. Appl. 36, 1267–1276 (2000). [CrossRef]
  5. L. M. Sanchez-Brea, P. Siegmann, M. A. Rebollo, and E. Bernabeu, “Optical technique for the automatic detection and measurement of surface defects on thin metallic wires,” Appl. Opt. 39, 539–545 (2000). [CrossRef]
  6. D.-C. Choi, Y.-J. Jeon, J. P. Yun, and S. W. Kim, “Pinhole detection in steel slab images using Gabor filter and morphological features,” Appl. Opt. 50, 5122–5129 (2011). [CrossRef]
  7. J. P. Yun, Y.-J. Jeon, D.-C. Choi, and S. W. Kim, “Real-time defect detection of steel wire rods using orthogonal wavelet filters optimized by univariate dynamic encoding algorithm for searches (uDEAS),” J. Opt. Soc. Am. A 29, 797–807 (2012). [CrossRef]
  8. A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002). [CrossRef]
  9. J. P. Yun, S. H. Choi, J. W. Kim, and S. W. Kim, “Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS),” NDT and E Int. 42, 389–397 (2009).
  10. N. A. Kaliteevsky, V. E. Semenov, V. D. Glezer, and V. E. Gauselman, “Algorithm of invariant image description by the use of a modified Gabor transform,” Appl. Opt. 33, 5256–5261 (1994). [CrossRef]
  11. D. Gabor, “Theory of communication,” J. Inst. Electr. Eng. London 93, 429–457 (1946).
  12. J. G. Daugman, “Uncertainty relation for resolution in space, spatial-frequency and orientation optimized by two-dimensional visual cortical filter,” J. Opt. Soc. Am. A 2, 1160–1169 (1985). [CrossRef]
  13. A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002). [CrossRef]
  14. D. Casasent and J. Smokelin, “Real, imaginary, and clutter Gabor filter fusion for detection with reduced false alarms,” Opt. Eng. 33, 2255–2263 (1994). [CrossRef]
  15. C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn. 20, 273–297 (1995).
  16. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,” 2003, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf .

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited