Plain Woven Fabric Defect Detection Based on Image Processing and Artificial Neural Networks

International Journal of Computer Trends and Technology (IJCTT)          
© - December Issue 2013 by IJCTT Journal
Volume-6 Issue-4                           
Year of Publication : 2013
Authors :Dr.G.M.Nasira , P.Banumathi


Dr.G.M.Nasira , P.Banumathi"Plain Woven Fabric Defect Detection Based on Image Processing and Artificial Neural Networks"International Journal of Computer Trends and Technology (IJCTT),V6(4):226-229 December Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract:- -Textile industry is one of the revenues generating industry to TamilNadu. The detection of defect in fabric is a major threat to textile industry. Woven fabrics are produced by weaving. Weaving is a process of interlacing two distinct yarns namely warps and weft. A fabric fault is any abnormality in the fabric that hinders its acceptability by the user. The price of the fabric is affected by the defects in fabric.At present, the fault detection is done manually after production of a sufficient amount of fabric. The nature of work is very dull and repetitive. There is a possibility of human errors with high inspection time in manual inspection, hence it is uneconomical. This paper proposed a computer based inspection system for identification of defects in the woven fabrics using image processing and Artificial Neural Network (ANN) with benefits of low cost and high detection rate. The inspection system first acquires high quality vibration free images of the fabric. Then the acquired images are first preprocessed and normalized using image processing techniques then the preprocessed image is converted into binary images. From the binary image first order statistical features are extracted and these extracted features are given to the input to the Artificial Neural Network (ANN) which uses back propagation algorithm to calculate the weighted factors and generates the output. The ANN is trained by using 115 defect free and defected images.


[1] YH Zhang, WK Wong (2011), “An Intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network”, Textile Research Journal October 2011 vol. 81 no. 17 1772-1787.
[2] Jayanta K. Chandra, Pradipta K. Banerjee & Asit K. Datta, “Neural network trained morphological processing for the detection of defects in woven fabric”, Journal of the Textile Institute ,Volume 101, Issue 8, 2010,pages 699-706.
[3] Yin KaiCheng, Yu Weidong, "Clothing Defect Detection System Based on Image Processing Technology", The Computer System Applications, 2008.10, pp.7-10.
[4] Ma HongLong, "Fabric Defect Detection Analysis and Design Based on Image Recognition " , Beijing University of Technology Master`s thesis, 2007.
[5] Lu Yun, Zhang Jingmiao, "FAbric Defect Detection Method Based on Image Distance Difference", Micro-computer Information, No 23,2007, pp.306-308.
[6] Arivazhagan S., Ganesan L. and Bama S. (2006), „Fault segmentation in fabric images using Gabor wavelet transform?, International Journal of Machine Vision and Applications, Vol. 16, No. 6, pp. 356-363.
[7] Arivazhagan S. and Ganesan L. (2003), „Texture Classification using Wavelet Transform?, Pattern Recognition Letters, Vol. 24, Nos. 9-10, pp.513 - 1521.
[8] T. S. Newman and A. K. Jain, “A survey of automated visual inspection”, Comput. Vis. Image Understanding 61, pp. 321-262, 1995.
[9] Dr.P Chellasamy,K.Karuppaiah “An analysis of growth and development of textile industry in India” ,
[10] Dr.G.M.Nasira, P.Banumathi, “Fabric Defect Detection using Neural Networks”, in the Journal of Research in Recent Trends in the month of December 2011, ISSN 2250 – 3951.
[11] P.Banumathi, Dr.G.M.Nasira, “Fabric Inspection System using Artificial Neural Networks”, in the International Journal of Computer Engineering Science” in the month of May 2012, ISSN 2250 – 3349.

Keywords:-Artificial Neural Network (ANN), Back Propagation Algorithm Fault Detection,, Feature Extraction. Image processing.