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