A lower envelope Weber contrast detection algorithm for steel bar surface pit defects

Abstract A lower envelope Weber contrast (LEWC) recognition algorithm is proposed to detect steel bar surface pit defects. Some present algorithms could detect these defects but they are limited by the size of pits. This kind of defect has uniform background intensity in column pixels of steel bar surface images while it is not the case in row pixels. The lower envelope of column pixels is used to eliminate the effect of high gray level peak value points, and Weber contrast is employed to make that all areas in one image have the same threshold to detect pit defects. Experimental results show that the proposed LEWC algorithm has a high detection accuracy rate which exceeds 98% for the pit defects with different sizes.

[1]  Khan M. Iftekharuddin,et al.  Detection and tracking of targets in infrared images using Bayesian techniques , 2009 .

[2]  Lonnie C. Ludeman,et al.  Fundamentals of Digital Signal Processing , 1986 .

[3]  Yung-Nien Sun,et al.  A computer vision system for automatic steel surface inspection , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[4]  Wu Bin Li,et al.  Review of Vision Real-Time Inspection Algorithm for Rolling Steel Surface Defects , 2011 .

[5]  Y. Y. Hung,et al.  Real-time monitoring of clamping force of a bolted joint by use of automatic digital image correlation , 2009 .

[6]  Chang-hou Lu,et al.  A local annular contrast based real-time inspection algorithm for steel bar surface defects , 2012 .

[7]  Sang Woo Kim,et al.  Development of Real-time Defect Detection Algorithm for High-speed Steel Bar in Coil(BIC) , 2006, 2006 SICE-ICASE International Joint Conference.

[8]  Qingli Li,et al.  Detection of physical defects in solar cells by hyperspectral imaging technology , 2010 .

[9]  M. Pietikäinen,et al.  A robust descriptor based on Weber’s Law , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Jing Li,et al.  On-Line Seam Detection in Rolling Processes Using Snake Projection and Discrete Wavelet Transform , 2007 .

[11]  Youngsu Park,et al.  Real-Time Defects Detection Algorithm for High-Speed Steel Bar in Coil , 2007 .

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

[13]  Zhao Peng.  Precise speed measurement using an interlaced scan image , 2011 .

[14]  Jianhong Shen,et al.  On the foundations of vision modeling: I. Weber’s law and Weberized TV restoration , 2003 .