DocumentCode :
2077591
Title :
Novel approaches for detecting fabric fault using Artificial Neural Network with K-fold validation
Author :
Andalib, Ahmed Shayer ; Islam, Md Rafiqul ; Salekin, A. ; Abdulla-Al-Shami, Md
Author_Institution :
Dept. of Comput. Sci. & Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh
fYear :
2012
fDate :
22-24 Dec. 2012
Firstpage :
55
Lastpage :
60
Abstract :
In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we have implemented Artificial Neural Network (ANN). Both of our newly proposed method and improved technique have outperformed the existing methods. We have implemented K-validation to estimate the performance of our classification model. Additionally we have analyzed the performance of our classification model for different experimental parameters. Finally we have presented a comparative analysis of these techniques.
Keywords :
fabrics; image classification; neural nets; object detection; production engineering computing; woven composites; ANN; K-fold validation; artificial neural network; classification model; fabric fault detection; fabric image; histogram processing; woven fabric; Adaptive Median Filter; Artificial Neural Network; K-Validation; Roberts Operator;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Technology (ICCIT), 2012 15th International Conference on
Conference_Location :
Chittagong
Print_ISBN :
978-1-4673-4833-1
Type :
conf
DOI :
10.1109/ICCITechn.2012.6509767
Filename :
6509767
Link To Document :
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