Abstract

Presently, product inspection for quality control is becoming an important part in the steel manufacturing industry. In this paper, we propose a vision-based method for detection of pinholes in the surface of scarfed slabs. The pinhole is a very tiny defect that is 1–5mm in diameter. Because the brightness in the surface of a scarfed slab is not uniform and the size of a pinhole is small, it is difficult to detect pinholes. To overcome the above-mentioned difficulties, we propose a new defect detection algorithm using a Gabor filter and morphological features. The Gabor filter was used to extract defective candidates. The morphological features are used to identify the pinholes among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective to detect pinholes in the surface of the scarfed slab.

© 2011 Optical Society of America

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  6. J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
    [CrossRef]
  7. J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
    [CrossRef]
  8. 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]
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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
  15. J. Weng, J. Zhong, and C. Hu, “Phase reconstruction of digital holography with the peak of the two-dimensional Gabor wavelet transform,” Appl. Opt. 48, 3308–3316 (2009).
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2009 (3)

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&E Int. 42, 389–397 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. Weng, J. Zhong, and C. Hu, “Phase reconstruction of digital holography with the peak of the two-dimensional Gabor wavelet transform,” Appl. Opt. 48, 3308–3316 (2009).
[CrossRef] [PubMed]

2008 (1)

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

2006 (2)

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

2005 (1)

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]

2002 (2)

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002).
[CrossRef]

2000 (2)

1994 (2)

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]

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] [PubMed]

1985 (1)

1946 (1)

D. Gabor, “Theory of communication,” J. Inst. Electr. Eng., Part 3 93, 429–441 (1946).
[CrossRef]

Benbelghit, A.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Bennamoun, M.

A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002).
[CrossRef]

Bernabeu, E.

Bodnarova, A.

A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002).
[CrossRef]

Boubenider, F.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Boutassouna, D.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Casasent, D.

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]

Chan, C.

C. Chan and G. K. H. Pang, “Fabric defect detection by Fourier analysis,” IEEE Trans. Ind. Appl. 36, 1267–1276 (2000).
[CrossRef]

Cho, C.-S.

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]

Choi, S. H.

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&E Int. 42, 389–397 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Chung, B.-M.

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]

Daugman, J. G.

Gabor, D.

D. Gabor, “Theory of communication,” J. Inst. Electr. Eng., Part 3 93, 429–441 (1946).
[CrossRef]

Gauselman, V. E.

Glezer, V. D.

Gonzalez, R. C.

R. C. Gonzalez and R. E. Wood, Digital Image Processing(Prentice Hall, 2002).

Guan, X. P.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Helifa, B.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Hu, C.

Huang, Q.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Kaliteevsky, N. A.

Kim, J. W.

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&E Int. 42, 389–397 (2009).
[CrossRef]

Kim, S. W.

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&E Int. 42, 389–397 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Kumar, A.

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

Latham, S.

A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002).
[CrossRef]

Lefkaiser, I. K.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Li, X.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Oulhadj, A.

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

Pang, G. K. H.

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

C. Chan and G. K. H. Pang, “Fabric defect detection by Fourier analysis,” IEEE Trans. Ind. Appl. 36, 1267–1276 (2000).
[CrossRef]

Park, M.-J.

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]

Rebollo, M. A.

Sanchez-Brea, L. M.

Semenov, V. E.

Seo, B.

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Siegmann, P.

Smokelin, J.

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]

Tso, S. K.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Weng, J.

Wood, R. E.

R. C. Gonzalez and R. E. Wood, Digital Image Processing(Prentice Hall, 2002).

Yun, J. P.

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&E Int. 42, 389–397 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Zhong, J.

Appl. Opt. (3)

IEEE Trans. Ind. Appl. (2)

C. Chan and G. K. H. Pang, “Fabric defect detection by Fourier analysis,” IEEE Trans. Ind. Appl. 36, 1267–1276 (2000).
[CrossRef]

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

IEEE Trans. Ind. Electron. (2)

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

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]

J. Inst. Electr. Eng., Part 3 (1)

D. Gabor, “Theory of communication,” J. Inst. Electr. Eng., Part 3 93, 429–441 (1946).
[CrossRef]

J. Opt. Soc. Am. A (1)

NDT&E Int. (2)

B. Helifa, A. Oulhadj, A. Benbelghit, I. K. Lefkaiser, F. Boubenider, and D. Boutassouna, “Detection and measurement of surface cracks in ferromagnetic materials using eddy current testing,” NDT&E Int. 39, 384–390 (2006).
[CrossRef]

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&E Int. 42, 389–397 (2009).
[CrossRef]

Opt. Eng. (3)

J. P. Yun, S. H. Choi, B. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-coverd steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

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]

Pattern Recogn. (1)

A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal Gabor filters for textile flaw detection,” Pattern Recogn. 35, 2973–2991 (2002).
[CrossRef]

Other (1)

R. C. Gonzalez and R. E. Wood, Digital Image Processing(Prentice Hall, 2002).

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Figures (10)

Fig. 1
Fig. 1

Structure of automatic inspection system.

Fig. 2
Fig. 2

(a) Steel slab image. (b) Vertical projection profile of (a).

Fig. 3
Fig. 3

Procedure of dividing subimages. (a) The first subimage. (b) The next subimage in the horizontal direction. (c) The next subimage in the vertical direction.

Fig. 4
Fig. 4

Pinhole images.

Fig. 5
Fig. 5

Gabor filtering: (a) input image, (b) Gabor-filtered image.

Fig. 6
Fig. 6

Defect candidates extraction: (a) image after Gabor1 filtering, (b) binarized image of (a), (c) image after Gabor2 filtering, (d) binarized image of (c), (e) combined image of (b) and (c), (f) defect candidates.

Fig. 7
Fig. 7

Proposed masks for the (a) bright and (b) dark regions.

Fig. 8
Fig. 8

Complete blob extraction: (a) blob image of defect candidate, (b) complete blob image that contains (a).

Fig. 9
Fig. 9

Relative distance and orientation.

Fig. 10
Fig. 10

Slab images containing pinholes: (a), (c), (e), and (g) show original images, (b), (d), (f), and (h) show images after detection.

Tables (1)

Tables Icon

Table 1 Parameters of Gabor Filters

Equations (15)

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P ( x ) = 1 N y = 1 N I ( x , y ) ,
f ( x , y ) = 1 2 π σ x σ y exp [ 1 2 ( x 2 σ x 2 + y 2 σ y 2 ) ] exp ( 2 π j u 0 x ) .
g ( x , y ) = 1 2 π σ x σ y exp [ 1 2 { ( x σ x ) 2 + ( y σ y ) 2 } ] sin ( 2 π j f x ) ,
x = x cos θ + y sin θ , y = x sin θ + y cos θ .
R ( x , y ) = g ( x , y ) * I ( x , y ) = m = 1 M 1 n = 1 N 1 g ( m , n ) I ( x m , y n ) ,
T = mean { R ( x , y ) } + α × std { R ( x , y ) } ,
If     T < R ( x , y ) then     B ( x , y ) = 1 Else     B ( x , y ) = 0 .
T high = mean { I sub ( x , y ) } + α high × std { I sub ( x , y ) } , T low = mean { I sub ( x , y ) } + α low × std { I sub ( x , y ) } ,
If     T high < I sub ( x , y ) then     B high ( x , y ) = 1 and B low ( x , y ) = 0 Else     T low > I sub ( x , y ) then     B high ( x , y ) = 0 and B low ( x , y ) = 1   Else     B high ( x , y ) = 0 and B low ( x , y ) = 0 .
If     Score high > 0 and Score low > 0 then     Score = Score high × Score low else     Score = 0 ,
Score high = 1 M N x = 1 M y = 1 N B high ( x , y ) × M high ( x , y ) , Score low = 1 M N x = 1 M y = 1 N B low ( x , y ) × M low ( x , y ) ,
θ = | arctan ( y high y low x high x low ) | ,
d = 2 ( x high x low ) 2 + ( y high y low ) 2 A high + A low ,
r area = A high A low A high + A low .
r extent = 1 2 ( A high B high + A low B low ) ,

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