Defects in textile products reduce the value of
textile industry in the world. Artificial Intelligence
techniques applied for defect identification in fabric
inspection of textile industry. An Artificial Neural Network
(ANN) technique is used in this paper for identifying defects
in textile products. The images to be analyzed is obtained
from image acquisition system and saved them in Joint
Photographic Experts Group (JPEG) format. Features are
extracted from the acquired image and feature selection
method is used to reduce the dimensionality of feature set by
creating new feature set of smaller size that are a
combination of old features. Multi Layer Back Propagation
algorithm is used to train and test the ANN.
Dr.P.Banumathi : received BE, MCA, M.Phil and MBA
in the year 1994, 2004, 2007 and 2008. She is
having 15 Years of teaching experience and 5 years
of Industrial experience. Her area of interest is
Artificial Neural Networks and Image Processing.
She has presented 15 technical papers in various
Seminars / National Conferences. She has
presented 3 technical papers in International Conference. She has
published 15 articles in International Journal. She is a member of
Indian Society for Technical Education (ISTE) and Computer Society
of India (CSI).
T.Sakthi Sree : Assistant Professor, Department of Computer Science and Engineering,
Kathir College of Engineering, Wisdom Tree, Neelambur, Avinashi Road, Coimbatore – 641062, TamilNadu
S.P.Vidhya Priya : Assistant Professor, Department of Computer Science and Engineering,
Kathir College of Engineering, Wisdom Tree, Neelambur, Avinashi Road, Coimbatore – 641062, TamilNadu
Artificial Neural Network (ANN)
Multi Layer
Back Propagation Algorithm
Image Acquisition
Feature
Extraction and Selection
In this paper, an Artificial Neural Network based defect
identification system for fabric images was implemented.
Creating an accurate method for fabric image analysis and
defect identification is a major problem faced by the
existing system. The implemented system identifies plainwoven
fabric defects 99% accurately. From the results
obtained by our proposed system indicate that a reliable
fabric defect identification system for textile industries
can be introduced.
In this paper all the acquired fabric images are of woven
fabrics. But often textile industries process various pattern
of fabrics. So, this fabric defect identification system can
have the scope of getting implemented in other types of
fabrics .
[1] Dr. G. M. Nasira, P. Banumathi (December 2011),
“Fabric Defect Detection using Neural Networks”,
Journal of Research in Recent Trends, December
2011, ISSN 2250 – 3951 (Online) | ISSN 2250 –
3943 (Print).
[2] P. Banumathi, Dr. G. M. Nasira, “Fabric Inspection
System using Artificial Neural Networks”,
International Journal of Computer Engineering
Science, May 2012 ISSN 2250 – 3439 Volume 2
Issue 5.
[3] Dr.P Chellasamy,K.Karuppaiah “An analysis of
growth and development of textile industry in India”
www.fibrer2fashion.com.
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