Local neighborhood analysis for fabric defect detection

This paper analysis the detection of defects in fabric based on Local Neighborhood Analysis. In the proposed algorithm full image is inspected by moving Local Neighborhood Window over it. For the homogeneity measure, Coefficient of variation is used. The value of coefficient of variation is maximum if there is defect in the fabric. Defected image is segmented by using thresholding. Morphological filtering on defect segmented image is used to enhance defective region. The computational efficiency increased by using integral image technique. It avoids complicated spectral decomposition. Image database include TILDA database and in-house database. Experiments are performed for defects likes slack end, loose weft, drop stitch, holes, broken end, missing plush loop etc.

[1]  Chi-Ho Chan,et al.  Fabric defect detection by Fourier analysis , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[2]  Xianghua Xie,et al.  A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques , 2008 .

[3]  Farhat Fnaiech,et al.  Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network , 2015 .

[4]  Gaurav Sharma,et al.  Automatic Fabric Fault Detection Using Morphological Operations on Bit Plane , 2013 .

[5]  R. Drobina,et al.  Application of the image analysis technique for textile identification , 2006 .

[6]  Luo Jin,et al.  A Review on Surface Defect Detection , 2014 .

[7]  Peihua Gu,et al.  Free-form surface inspection techniques state of the art review , 2004, Comput. Aided Des..

[8]  Bo Hsiao,et al.  Automatic surface inspection using wavelet reconstruction , 2001, Pattern Recognit..

[9]  Xinhua Chen,et al.  Surface Defect Detection Algorithm Based on Local Neighborhood Analysis , 2015, ITITS.

[10]  Farhat Fnaiech,et al.  Fabric defect detection using local homogeneity and morphological image processing , 2016, 2016 International Image Processing, Applications and Systems (IPAS).

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

[12]  Ajay Kumar,et al.  Defect detection in textured materials using Gabor filters , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

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

[14]  Ajay Kumar,et al.  Fabric defect segmentation using multichannel blob detectors , 2000 .

[15]  Runping Han,et al.  Fabric Defect Detection Method Based on Gabor Filter Mask , 2009, 2009 WRI Global Congress on Intelligent Systems.

[16]  Li Ma,et al.  Optimum Gabor filter design and local binary patterns for texture segmentation , 2008, Pattern Recognit. Lett..

[17]  Farhat Fnaiech,et al.  Fabric defect localization using line variances of the local homogeneity images , 2015, Comput. Syst. Sci. Eng..