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  Artificial Intelligence Techniques in Textile Fabric Inspection  
  Authors : Dr.P.Banumathi; T.Sakthi Sree; S.P.Vidhya Priya
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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.


Published In : IJCSN Journal Volume 4, Issue 5

Date of Publication : October 2015

Pages : 793 - 798

Figures :05

Tables : 02

Publication Link : Artificial Intelligence Techniques in Textile Fabric Inspection




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 .










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