Intelligent detection of defects of yarn-dyed fabrics by energy-based local binary patterns

For the purpose of realizing fast and effective detection of defects of yarn-dyed fabric via computer vision, and in consideration of the inherent characteristics of texture, that is, color and structure, an applicable approach for intelligent defect detection is proposed in this paper. The image of yarn-dyed fabric enhanced by fractional differentials is first converted from RGB true color space to L*a*b* color space, and energy-based feature images are acquired after the Log-Gabor filter filters chromatic and brightness channels. Then the paper defines the relations between energy and the local binary pattern as a new concept called energy-based local binary patterns (ELBPs). Finally defects can be detected, using ELBPs rather than grayscale-based local binary patterns. The proposed method can detect chromatic and structural defects. Experimental results for the defect detection from several collections of yarn-dyed fabrics indicate that a detection success rate of more than 94.09% is achieved for the proposed method, and the speed of test is also fast.

[1]  Mohammed Bennamoun,et al.  Defect detection in textile materials based on aspects of the HVS , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[2]  Zhigang Fan,et al.  Automated Inspection of Textile Fabrics Using Textural Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[4]  Bugao Xu,et al.  A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects , 2009, Machine Vision and Applications.

[5]  Stavros A. Koubias,et al.  Real-Time Vision-Based System for Textile Fabric Inspection , 2001, Real Time Imaging.

[6]  James S. Goddard,et al.  Vision system for on-loom fabric inspection , 1999 .

[7]  Ehsanollah Kabir,et al.  Fabric Defect Detection Using Modified Local Binary Patterns , 2008, EURASIP J. Adv. Signal Process..

[8]  D. Tsai,et al.  Defect detection in coloured texture surfaces using Gabor filters , 2005 .

[9]  S. Ozdemir,et al.  Markov random fields and Karhunen-Loeve transforms for defect inspection of textile products , 1996, Proceedings 1996 IEEE Conference on Emerging Technologies and Factory Automation. ETFA '96.

[10]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[11]  Mohammed Bennamoun,et al.  Suitability Analysis of Techniques for Flaw Detection in Textiles using Texture Analysis , 2000, Pattern Analysis & Applications.

[12]  M. Mirmehdi,et al.  TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Hamed Sari-Sarraf,et al.  A generalized development environment for inspection of web materials , 1997, Proceedings of International Conference on Robotics and Automation.

[14]  Jeng-Jong Lin,et al.  Pattern Recognition of Fabric Defects Using Case-Based Reasoning , 2010 .

[15]  Randall R. Bresee,et al.  Fabric Defect Detection and Classification Using Image Analysis , 1995 .

[16]  E. Shady,et al.  Detection and Classification of Defects in Knitted Fabric Structures , 2006 .

[17]  Ajay Kumar,et al.  Computer-Vision-Based Fabric Defect Detection: A Survey , 2008, IEEE Transactions on Industrial Electronics.

[18]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jian Wei Li,et al.  Image Enhancement Based on Fractional Differential and Image Entropy , 2010 .

[20]  K. F. C. Yiu,et al.  Fabric defect detection using morphological filters , 2009, Image Vis. Comput..

[21]  Michael K. Ng,et al.  Wavelet based methods on patterned fabric defect detection , 2005, Pattern Recognit..

[22]  Mohammed Bennamoun,et al.  Automatic visual inspection and flaw detection in textile materials: past, present and future , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[23]  Ajay Kumar,et al.  Neural network based detection of local textile defects , 2003, Pattern Recognit..

[24]  Honggang Bu,et al.  Detection of Fabric Defects by Auto-Regressive Spectral Analysis and Support Vector Data Description , 2010 .

[25]  Jeng-Jong Lin,et al.  Applying an Artificial Neural Network to Pattern Recognition in Fabric Defects , 1995 .

[26]  Adrian E. Raftery,et al.  Linear flaw detection in woven textiles using model-based clustering , 1997, Pattern Recognit. Lett..

[27]  Du-Ming Tsai,et al.  Automated surface inspection for directional textures , 1999, Image Vis. Comput..

[28]  Wing-Keung Wong,et al.  An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network , 2011 .