Automated steel bridge coating rust defect recognition method based on color and texture feature

Abstract Most acquired steel bridge coating images are classified into two groups, defect and non-defect, by naked eyes or using the method reported in Lee's work [18]. Lee's method works effectively with blue-paint-coated steel bridge images in the State of Indiana, U.S.A. However, its effect on other coating colors or in some particular conditions, such as coating images with background noise or non-uniform illumination, has not yet been explored. In view of this, a rust defect recognition method based on color and texture feature (RUDERM), which combines the Fourier transform and color image processing, is proposed in this research. RUDERM aims to adapt to various background colors and overcome the influences from particular conditions. After comparisons of processed results, it is proven that RUDERM has an advantage in handling non-uniform illumination and can achieve a shorter processing time, which may lead to real-time coating inspection in the near future.

[1]  Luh-Maan Chang,et al.  Box-and-Ellipse-Based ANFIS for Bridge Coating Assessment , 2010, J. Comput. Civ. Eng..

[2]  Sangwook Lee,et al.  Automated recognition of surface defects using digital color image processing , 2006 .

[3]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[4]  Bayan S. Sharif,et al.  Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa , 1998, IEEE Transactions on Information Technology in Biomedicine.

[5]  Hirobumi Nishida.  Structural feature indexing for retrieval of partially visible shapes , 2002, Pattern Recognit..

[6]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[7]  S. F. Stiemer,et al.  Development of Bridge Corrosion Cost Model for Coating Maintenance , 1996 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Luh-Maan Chang,et al.  Support-vector-machine-based method for automated steel bridge rust assessment , 2012 .

[10]  Yassir AbdelRazig.  *Construction quality assessment: A hybrid decision support model using image processing and neural learning for intelligent defects recognition , 1999 .

[11]  Kai-Kuang Ma,et al.  Fuzzy color histogram and its use in color image retrieval , 2002, IEEE Trans. Image Process..

[12]  Po-Han Chen,et al.  INTELLIGENT STEEL BRIDGE COATING ASSESSMENT USING NEURO-FUZZY RECOGNITION APPROACH , 2002 .

[13]  Terry Caelli,et al.  On the classification of image regions by colour, texture and shape , 1993, Pattern Recognit..

[14]  Chengjun Liu,et al.  Comparative assessment of content-based face image retrieval in different color spaces , 2005, Int. J. Pattern Recognit. Artif. Intell..

[15]  Sangwook Lee.  Digital color image processing system for civil infrastructure health assessment and monitoring: Steel bridge coating case , 2005 .

[16]  A. Murat Tekalp,et al.  Shape similarity matching for query-by-example , 1998, Pattern Recognit..

[17]  Bertrand Zavidovique,et al.  Content based image retrieval using motif cooccurrence matrix , 2004, Image Vis. Comput..

[18]  Luh-Maan Chang,et al.  Automated bridge coating defect recognition using adaptive ellipse approach , 2009 .

[19]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..