Call For Paper Volume:4 Issue:10 Oct'2017 |

Image Processing and Artificial Neural Network techniques in Identifying Defects of Textile Products

Publication Date : 27/07/2015



Author(s) :

P.BANUMATHI , T S USHANANDHINI.


Volume/Issue :
Volume 2
,
Issue 8
(07 - 2015)



Abstract :

This paper proposes an Artificial  Neural Network based defect identifier for identifying defects in woven  fabric  images of the textile industry. Image processing is  the upcoming technique to enhance raw images into meaningful information. Image processing is used in various applications where in  textile industry is one of the main application area of it. Defects in fabric is a  major threat to textile industry. A fabric defect is any abnormality in the fabric that hinders its acceptability by the user and hence the  price of the fabric is affected. The defect detection which is done manually is cumbersome, not very accurate and  time consuming. Hence automated defect identification system becomes essential in textile industries. A detailed literature survey reveals that a liitle attention has been paid to automatic defect identification in woven fabric images. Hence an attempt has been made using  image processing and ANN techniques for defect identification in woven fabric images. A total of 200  woven fabric images are taken as samples, out of which 150 are used for training and 50 used for testing the network. These   images are preprocessed and normalized using image processing techniques and converted into binary image by taking intensity value as threshold. Six statistical values  namely mean, standard deviation, smoothness, energy, entropy and skewness are calculated  from the binary image. An ANN structure which consists of  (i) input layer having 6 neurons, (ii) one hidden layer having 5 neurons and(iii). Output layer having one neuron have been used. The training of the net is done using MATLAB 2012. The error percentage   in identifying the defect images was less than 1%, which is well accepted.


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Image Processing and Artificial Neural Network techniques in Identifying Defects of Textile Products

July 25, 2015