Real-Time Defects Detection Algorithm for High-Speed Steel Bar in Coil
This paper presents a real-time defect detection
algorithm for high-speed steel bar in coil. Because the target speed is
very high, proposed algorithm should process quickly the large
volumes of image for real-time processing. Therefore, defect detection
algorithm should satisfy two conflicting requirements of reducing the
processing time and improving the efficiency of defect detection. To
enhance performance of detection, edge preserving method is
suggested for noise reduction of target image. Finally, experiment
results show that the proposed algorithm guarantees the condition of
the real-time processing and accuracy of detection.
[1] Eduardo Abreu, Michael Lightstone, Sanjit K. Mitra and Kaoru Arakawa,
"A new Efficient Approach for the Removal of Impulse Noise from
Highly corrupted Images", IEEE Trans. Image processing. vol. 5, No. 6,
June 1996.
[2] Tzu-Chao Lin and Pao-Ta Yu, "Salt-Pepper Impulse Noise Detection and
Removal Using Multiple thresholds for Image Restoration", journal of
information science and engineering.
[3] M.Emin Yuksel, "A Hybrid Neuro-Fuzzy Filter for Edge Preserving
Restoration of Images Corrupted by Impulse Noise", IEEE trans. image
processing, vol.9, No. 11, November 2002.
[4] Shuqun Zhang, Mohammad A.Karim, "A New Impulse Detector for
Switching Median Filters", IEEE signal processing letter, Vol.9, No.11,
November 2002.
[5] Bryan W.Reutter, V.Ralph Algazi, and Ronald h.Huesman, "Nonlinear
Edge Preserving Smoothing and Segmentation of 4-D Medical Images via
Scale-Space Fingerprint Analysis", IPIM 2001: 17th International
conference, page 431-437.
[6] Keesug Choi, Kyungmo Koo and Jin S.Lee, "Development of Defect
Classification Algorithm for POSCO Rolling Strip Surface Inspection
System",SICE-ICASE International Joint Conference 2006.
[7] Ge-Wen Kang, Hong-Bing Liu, "Surface defects inspection of cold rolled
strips based on neural network", Machine Learning and Cybernetics,
Proceedings of 2005 International Conference, vol. 8, pp. 5034-5037,
2005.
[8] Mak, K.L., Peng, P., Lau, H.Y.K., "A Real-time Computer Vision System
for Detecting Defects in Textile Fabrics", Industrial Technology, ICIT
2005. IEEE International Conference, pp. 469-474, 2005.
[9] Rimac-Drlje, S., Keller, A., Hocenski, Z., "Neural network based
detection of defects in texture surfaces", Industrial Electronics, ISIE
2005. Proceedings of the IEEE International Symposium, vol. 3, pp. 1255
- 1260, 2005.
[10] Eren, L., Karahoca, A., Devaney, M.J., "Neural network based motor
bearing fault detection", Instrumentation and Measurement Technology
Conference, IMTC 04. Proceedings of the 21st IEEE, Vol. 3, pp. 1657 -
1660, 2004.
[11] S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique for digital
communications channel equalization using radial basis function
networks," IEEE Trans. Neural Networks, vol. 4, pp. 570-578, July 1993.
[12] Mandriota, C., Stella, E., Nitti, M., Ancona, N., Distante, A., "Rail
corrugation detection by Gabor filtering", Image Processing,
Proceedings. 2001 International Conference, vol. 2, pp. 626 - 628, 2001.
[13] Jae-Seung Ryu, Jong-Hwan Oh, Jeong-Goo Kim, Tak-Mo Koo,
Kil-Houm Park, "TFT-LCD panel Blob-Mura inspection using the
correlation of wavelet coefficients", TENCON 2004. IEEE Region 10
Conference, vol. A, pp. 219 - 222, 2004.
[14] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing,
Prentice Hall, 2002.
[15] Hongbin Jia, Murphey, Y.L., Jinajun Shi, Tzyy-Shuh Chang, "An
intelligent real-time vision system for surface defect detection", Pattern
Recognition, ICPR 2004. Proceedings of the 17th International
Conference, vol. 3, pp. 239 - 242, 2004.
[16] Takao Sugimoto, Tadao Kawaguchi, "Development of a surface defect
inspection system using radiant light from steel products in a hot rolling
line", IEEE Transactions on instrumentation and Measurement, vol. 47,
pp. 409 - 416, 1998.
[1] Eduardo Abreu, Michael Lightstone, Sanjit K. Mitra and Kaoru Arakawa,
"A new Efficient Approach for the Removal of Impulse Noise from
Highly corrupted Images", IEEE Trans. Image processing. vol. 5, No. 6,
June 1996.
[2] Tzu-Chao Lin and Pao-Ta Yu, "Salt-Pepper Impulse Noise Detection and
Removal Using Multiple thresholds for Image Restoration", journal of
information science and engineering.
[3] M.Emin Yuksel, "A Hybrid Neuro-Fuzzy Filter for Edge Preserving
Restoration of Images Corrupted by Impulse Noise", IEEE trans. image
processing, vol.9, No. 11, November 2002.
[4] Shuqun Zhang, Mohammad A.Karim, "A New Impulse Detector for
Switching Median Filters", IEEE signal processing letter, Vol.9, No.11,
November 2002.
[5] Bryan W.Reutter, V.Ralph Algazi, and Ronald h.Huesman, "Nonlinear
Edge Preserving Smoothing and Segmentation of 4-D Medical Images via
Scale-Space Fingerprint Analysis", IPIM 2001: 17th International
conference, page 431-437.
[6] Keesug Choi, Kyungmo Koo and Jin S.Lee, "Development of Defect
Classification Algorithm for POSCO Rolling Strip Surface Inspection
System",SICE-ICASE International Joint Conference 2006.
[7] Ge-Wen Kang, Hong-Bing Liu, "Surface defects inspection of cold rolled
strips based on neural network", Machine Learning and Cybernetics,
Proceedings of 2005 International Conference, vol. 8, pp. 5034-5037,
2005.
[8] Mak, K.L., Peng, P., Lau, H.Y.K., "A Real-time Computer Vision System
for Detecting Defects in Textile Fabrics", Industrial Technology, ICIT
2005. IEEE International Conference, pp. 469-474, 2005.
[9] Rimac-Drlje, S., Keller, A., Hocenski, Z., "Neural network based
detection of defects in texture surfaces", Industrial Electronics, ISIE
2005. Proceedings of the IEEE International Symposium, vol. 3, pp. 1255
- 1260, 2005.
[10] Eren, L., Karahoca, A., Devaney, M.J., "Neural network based motor
bearing fault detection", Instrumentation and Measurement Technology
Conference, IMTC 04. Proceedings of the 21st IEEE, Vol. 3, pp. 1657 -
1660, 2004.
[11] S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique for digital
communications channel equalization using radial basis function
networks," IEEE Trans. Neural Networks, vol. 4, pp. 570-578, July 1993.
[12] Mandriota, C., Stella, E., Nitti, M., Ancona, N., Distante, A., "Rail
corrugation detection by Gabor filtering", Image Processing,
Proceedings. 2001 International Conference, vol. 2, pp. 626 - 628, 2001.
[13] Jae-Seung Ryu, Jong-Hwan Oh, Jeong-Goo Kim, Tak-Mo Koo,
Kil-Houm Park, "TFT-LCD panel Blob-Mura inspection using the
correlation of wavelet coefficients", TENCON 2004. IEEE Region 10
Conference, vol. A, pp. 219 - 222, 2004.
[14] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing,
Prentice Hall, 2002.
[15] Hongbin Jia, Murphey, Y.L., Jinajun Shi, Tzyy-Shuh Chang, "An
intelligent real-time vision system for surface defect detection", Pattern
Recognition, ICPR 2004. Proceedings of the 17th International
Conference, vol. 3, pp. 239 - 242, 2004.
[16] Takao Sugimoto, Tadao Kawaguchi, "Development of a surface defect
inspection system using radiant light from steel products in a hot rolling
line", IEEE Transactions on instrumentation and Measurement, vol. 47,
pp. 409 - 416, 1998.
@article{"International Journal of Information, Control and Computer Sciences:56453", author = "Se Ho Choi and Jong Pil Yun and Boyeul Seo and YoungSu Park and Sang Woo Kim", title = "Real-Time Defects Detection Algorithm for High-Speed Steel Bar in Coil", abstract = "This paper presents a real-time defect detection
algorithm for high-speed steel bar in coil. Because the target speed is
very high, proposed algorithm should process quickly the large
volumes of image for real-time processing. Therefore, defect detection
algorithm should satisfy two conflicting requirements of reducing the
processing time and improving the efficiency of defect detection. To
enhance performance of detection, edge preserving method is
suggested for noise reduction of target image. Finally, experiment
results show that the proposed algorithm guarantees the condition of
the real-time processing and accuracy of detection.", keywords = "Defect detection, edge preserving filter, real-time image processing, surface inspection.", volume = "1", number = "1", pages = "108-5", }
