Defect Detection in Alloy Steel Surface Using Nonsubsampled Contourlet Transform

Defect Detection in Alloy Steel Surface Using Nonsubsampled Contourlet Transform

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-7 Number-2                          
Year of Publication : 2014
Authors : N.Vimalraj , Dr.B.Giriraj
  10.14445/22315381/IJETT-V7P238

citation 

N.Vimalraj , Dr.B.Giriraj , Article:Defect Detection in Alloy Steel Surface Using Nonsubsampled Contourlet Transform, International Journal of Engineering Trends and Technology(IJETT), 7(2),57-60, published by seventh sense research group

Abstract

Surface defect detection of metallic surfaces is a major challenge in any manufacturing industry. In this paper, an automated system to classify alloy steel surface based on Nonsubsampled Contourlet Transform (NSCT) is presented. Firstly the images are decomposed into different scales and directional subbands using Nonsubsampled Contourlet Transform (NSCT). The nonsubsampled contourlet transform is built upon nonsubsampled pyramids and nonsubsampled directional filter banks and provides a shift invariant directional multiresolution image representation. The image is decomposed at various scales and directions and the energy features are extracted. The energy features of defect and non defective surface are extracted and the best set that distinguishes the surface is used for classification.

References

[1]. Elmougy, Samir, Ibrahim El-Henawy, and Ahmed El-Azab. "Model Based Ceramic tile inspection using Discrete Wavelet Transform and Euclidean Distance." arXiv preprint arXiv:1003.1811 (2010).

[2]. A Zheng, Hong, Ling Xue Kong, and Saeid Nahavandi. "Automatic inspection of metallic surface defects using genetic algorithms." Journal of materials processing technology, 2002: pp427-433.

[3]. Du-Ming Tsai,Yuan-Ze Univ., Taoyuan ,Taiwan,Yung Chiang and Ya Hui Tsai, “A shift tolerant dissimilarity measure for surface defect detection”, IEEE Transactions on Industrial Informatics,vol.8, no.1, 2012, pp 128 – 137.

[4]. Du-Ming Tsai ,Yuan-Ze Univ, Taoyuan and Taiwan Jie-Yu Luo, “Mean shift based defect detection in multicrystalline solar wafer surfaces”, IEEE Transactions on Industrial Informatics, vol.7, no.1, 2011, pp 125 – 135.

[5]. Ghorai.S, Singh. R and Gangadaran. M, “Wavelet versus contourlet features for automatic defect detection on hot rolled steel sheet”, Third International Conference on Emerging Applications of Information Technology (EAIT), 2012, pp 149 – 152.

[6]. Jiwon Choi and Changick Kim, “Unsupervised detection of surface defects: A two-step approach”, 19th IEEE International Conference Image Processing (ICIP), 2012, pp 1037 – 1040.

[7]. Liang Wang, Yaping Hang,Siwei Luo, Xiaoyue Luo and Xinlan Jiang, “A new cluster based feature ex-traction method for surface defect detection: De-blurring Gaussian blur images: A preprocessing for rail head surface defect detection” , IEEE Interna-tional Conference on Service Operations, Logistics, and Informatics, 2011, pp 451 – 456.

[8]. Jiaoyan Ai and Xuefeng Zhu, “Analysis and detection of ceramic glass surface defects based on computer vision, Proceedings of the 4th World Congress on Intelligent Control and Automation”, vol.4, 2002, pp 3014-3018

[9]. Jianyun Ni, Jing Luo, Zaiping Chen and Enzeng Dong, “A multi resolution method for detecting defects in fabric images”, Research Journal of Applied Sciences, Engineering and Technology vol.5,no.5, 2013, pp 1689-1694.

[10]. Moasheri, B. B. M., and S. Azadinia. "A new voting approach to texture defect detection based on multiresolutional decomposition." World Academy of Science, Engineering, and Technology 73 (2011): 657-661.    

[11]. Xu, Ke, Yong-hao Ai, and Xiu-yong Wu. "Application of multi-scale feature extraction to surface defect classification of hot-rolled steels." International Journal of Minerals, Metallurgy, and Materials 20.1 (2013): 37-41.

[12]. M. N. Do and M. Vetterli, "The contourlet transform: an efficient directional multiresolution image representation, "IEEE Trans. Image Process, vol. 14, pp. 2091-2106, 2005.

[13]. R. R. Coifman and D. L. Donoho, Translation invariant de-noising: Wavelets and statistics. NewYork: Springer-Verlag, 1995.

[14]. A. L. Da Cunha, J. Zhou, and M. N. Do, "The Nonsubsampled Contourlet Transform: Theory, Design, and Applications," IEEE Trans. Image Process, vol. 15, pp.3089-3101, 2006.

Keywords:
NSCT, image classification, sub-band features, alloy steel surface.