Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks

Karayiannis, Y.; Stojanović, Radovan; Mitropoulos, Panagiotis; Koulamas, Christos; Stouraitis, T.; Koubias, Stavros; Papadopoulos, Georgios Th.

doi:10.1109/icecs.1999.813221

Cited by 47 publications

(49 citation statements)

References 7 publications

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“…(3) resolution of input image [19,[37][38][39][40][41]: e.g., a low resolution image cannot show fine defects in fabric; (4) alignment of input image [3,42,43]: e.g., misalignment in image acquisition induces false defect detection in template matching approach; (5) size [37,44], and shape [45][46][47] of defects: e.g., defect of small size or defect similar to a pattern shape increases difficulties in recognition; (6) speed or computation complexity of defect detection [37,48,49]: e.g., long learning delays may not be practical; (7) lighting [13]: e.g., improper illumination yields poor resolution and contrast; and (8) image acquisition techniques: e.g., most inspection methods use digital cameras to capture images. However, alternative approaches are also available, such as near-infrared (NIR) [50], X-ray, multispectral imaging and polarimetry, which may provide extra features in defect detection.…”

Section: Performance Metric For Defect Detectionmentioning

confidence: 99%

Automated fabric defect detection—A review

Ngan

Pang

Yung

2011

Image and Vision Computing

437

190

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Section: Performance Metric For Defect Detectionmentioning

confidence: 99%

Automated fabric defect detection—A review

Ngan

Pang

Yung

2011

Image and Vision Computing

437

190

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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%

“…In both cases, they have used six types of defects. Karayiannis et al [20] have used seven types of defect. They have used statistical texture features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Backpropagation learn ing algorith m has been used in [18], [19], [20], [21] and [22]. Habib and Rokonuzzaman [18] have worked with knitted fabrics.…”

Section: Comparative Performance Analysismentioning

confidence: 99%

“…The accuracy has been good although the model co mplexity and training time have not been mentioned. Karay iannis et al [20] have worked with web text ile fabrics. They have used a three-layer NN, wh ich had 13, 5 and 8 co mputing units in the input, hidden and output layers, respectively.…”

Section: Comparative Performance Analysismentioning

confidence: 99%

See 2 more Smart Citations

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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NN Automated Defect Detection Based on Optimized Thresholding

Castilho¹,

Pinto²,

Serafim

2006

Lecture Notes in Computer Science

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This paper presents a new contribution for the problem of automatic visual inspection. New methods for determining threshold values for fabric defect detection using feedforward neural networks are proposed. Neural networks are one of the fastest most flexible classification systems in use. Their implementation in defect detection, where a clear classification is needed, requires thresholding the output. Two methods are proposed for threshold selection, statistical analysis of the NN output and confusion matrix based optimization. Experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have confirmed their usefulness.

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Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks

Cited by 47 publications

References 7 publications

Automated fabric defect detection—A review

Automated fabric defect detection—A review

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

NN Automated Defect Detection Based on Optimized Thresholding

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