Fabric Defect Detection Based on Computer Vision

Sun, Jing; Zhou, Zhiyu

doi:10.1007/978-3-642-23896-3_11

Cited by 2 publications

(1 citation statement)

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“…Likewise NNs have been involved in the research of automated fabric defect inspection system. Many efforts have been given for auto mated fabric defect inspection [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Most of them have focused on defect detection, where few of them have focused on classification.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

Habib¹,

Rokonuzzaman²

2014

IJISA

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Abstract-Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.

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