Vision-based defect detection of scale-covered steel billet surfaces

Vision-based inspection systems have been widely investigated for the detection and classification of defects in various industrial product. We present a new defect detection algorithm for scale-covered steel billet surfaces. Because of the availability of various kinds of steel, presence of scales, and manufacturing conditions, the features of billet surface images are not uniform. In particular, scales severely change the properties of defect-free surfaces. Moreover, the various kinds of possible defects make their detection difficult. In order to resolve these problems and to improve the detection performance, two methods are proposed. First, undecimated wavelet transform and vertical projection profile are presented. Second, a method for detecting the variations in the block features along the vertical direction is proposed. The former method can effectively detect vertical line defects, and the latter can efficiently detect the remaining defects, except the vertical line defects. The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces.

[1]  Yi Sun,et al.  Real-time automatic detection of weld defects in steel pipe , 2005 .

[2]  Pengfei Shi,et al.  An adaptive level-selecting wavelet transform for texture defect detection , 2007, Image Vis. Comput..

[3]  Kil-Houm Park,et al.  A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System , 2008, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[4]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  Yang Tao,et al.  Brightness-invariant image segmentation for on-line fruit defect detection , 1998 .

[6]  Wei Liu,et al.  Joint transform correlator for the detection of defects in optical fibers , 1998 .

[7]  Franz Pernkopf,et al.  Detection of surface defects on raw steel blocks using Bayesian network classifiers , 2004, Pattern Analysis and Applications.

[8]  Z Chen,et al.  Multiresolution local contrast enhancement of x-ray images for poultry meat inspection. , 2001, Applied optics.

[9]  Jonathan G. Campbell,et al.  Automatic visual inspection of woven textiles using a two-stage defect detector , 1998 .

[10]  Grantham K. H. Pang,et al.  Fabric defect segmentation using multichannel blob detectors , 2000 .

[11]  Ge-Wen Kang,et al.  Surface defects inspection of cold rolled strips based on neural network , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Qian Huang,et al.  Improving Automatic Detection of Defects in Castings by Applying Wavelet Technique , 2006, IEEE Transactions on Industrial Electronics.

[13]  Wei Liu,et al.  Image Fusion Based on PCA and Undecimated Discrete Wavelet Transform , 2006, ICONIP.

[14]  SungHoo Choi,et al.  Real-time vision-based defect inspection for high-speed steel products , 2008 .

[15]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

[16]  Che-Seung Cho,et al.  Development of real-time vision-based fabric inspection system , 2005, IEEE Transactions on Industrial Electronics.

[17]  Zude Zhou,et al.  An online defects inspection method for float glass fabrication based on machine vision , 2008 .

[18]  Franz Pernkopf,et al.  Visual Inspection of Machined Metallic High-Precision Surfaces , 2002, EURASIP J. Adv. Signal Process..