Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

doi:10.1109/ICCCNT.2012.6396004

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Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

Proceedings Article•DOI•

Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

Anushree Basu, Jayanta K. Chandra, Pradipta K. Banerjee, S. Bhattacharyya, Asit K. Datta¹ - Show less +1 more•Institutions (1)

University of Calcutta¹

26 Jul 2012-pp 1-6

TL;DR: The sub image based PCA method is applied for the extraction of the feature from the training and test fabric images and the multi-class SVM classifier is used for carrying out the classification task.

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Abstract: Human visual system can identify larger defects taking place on the woven fabric. But it is very difficult to classify and identify the small fabric defects by a human inspector. In the textile industries the defect detection by a human inspector affects the production tremendously. Thus this paper gives a solution of this problem by developing an automatic fabric defect detection system, based on the computer vision. The sub image based PCA method is applied for the extraction of the feature from the training and test fabric images and the multi-class SVM classifier is used for carrying out the classification task. The method is tested on the standard TILDA database of fabric defect and a success rate of 96.36% is achieved.

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Journal Article•DOI•

Fabric defect detection systems and methods—A systematic literature review

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Kazım Hanbay¹, Muhammed Fatih Talu², Ömer Faruk Özgüven²•Institutions (2)

Bingöl University¹, İnönü University²

01 Dec 2016-Optik

TL;DR: A comprehensive literature review of fabric defect detection methods, categorized into seven classes as structural, statistical, spectral, model-based, learning, hybrid and comparison studies, finds weaknesses of each approach.

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150 citations

Journal Article•DOI•

Kumaş hatalarinin online/offline tespit sistemleri ve yöntemleri

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Kazım Hanbay, M. Fatih Talu

09 Apr 2014

TL;DR: An state-of-the-art survey of different defect detection methods is offered and their characteristics, mathematical formulation, strengths and weaknesses are described and a qualitative analysis accompanied by results are presented.

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Abstract: Fabric defect detection is a vital step of quality control in the textile manufacturing industry. This paper firstly offers an state-of-the-art survey of different defect detection methods and describes their characteristics, mathematical formulation, strengths and weaknesses. Secondly, it employs a wider classification of methods and divides them into seven approaches (structural, statistical, spectral, model-based, learning, hybrid, and comparative) and performs a comparative study across these methods. Thirdly, it also presents a qualitative analysis accompanied by results, including detection success rate, rotation/scaling invariant, reliability and noise sensitivity

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11 citations

Additional excerpts

...Shi ve arkadaşları [94], 2048 sensör dizisine sahip çizgi tarama kamera ve TILDA [95] kumaş veri tabanından elde ettikleri kumaş görüntülerindeki hataların bölütlenmesi için YSA kullanmıştır....
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...Bazı çalışmalar TILDA [95] kumaş veri tabanını kullanmışlardır....
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...[97] Fitilli Bilinmiyor Offline Kullanılmıyor Bilinmiyor [40] Düz, fitilli, kot, dokuma 9 farklı hata tipi Offline ve online Bilinmiyor %97.40 [98,99] Jakar 6 farklı hata tipi Offline Bilinmiyor %96.70 [41] Düz dokuma 3 farklı hata tipi Offline Hata arama algoritması %79.10 [14] Örüntüsüz Bilinmiyor Online Kullanılmıyor Bilinmiyor [100] 7 farklı kumaş tipi Çözgü Offline Bilinmiyor Bilinmiyor [102] TILDA’dan 3 farklı kumaş tipi TILDA’dan 9 hata tipi Offline Bilinmiyor %98.8 g. Karşılaştırma Çalışmaları (Comparative Studies) Literatürde çok sayıda kumaş hatası tespit metodu olduğu için, bu metotlar arasında karşılaştırmalar yapılması önem kazanmaktadır....
[...]
...[90] Fitilli, düz 5 farklı hata tipi Offline ve online İleri beslemeli YSA Bilinmiyor [91] Dokuma Yok Offline YSA %99.20 [92] Örgü Yok Offline YSA %97.20 [93] Örgü 5 farklı hata tipi Offline Geri yayılımlı YSA %100 [94] Dokuma TILDA’dan 17 hata tipi Offline Adım birleştirmeli YSA Bilinmiyor [96] Dokuma TILDA’dan 9 hata tipi Offline SVM %96.36 f. Melez Yaklaşımlar (Hybrid Approaches) Otomatik bir kumaş hatası tespit yöntemi üstün yanlara sahip iken aynı zamanda bazı noktalarda da eksiklik kalabilmektedir....
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...TILDA veri tabanından elde ettikleri kumaş görüntülerinin Gabor filtreleri ile özniteliklerini çıkartarak Temel Bileşenler Analizi yöntemi ile özellik boyutunu indirgemişlerdir....
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Journal Article•DOI•

Fabric defect detection based on separate convolutional UNet

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Le Cheng, Ji Zheng Yi, Aibin Chen, Yi Zhang

29 Jul 2022-Multimedia Tools and Applications

8 citations

Journal Article•DOI•

Filamentous target segmentation of weft micro-CT image based on U-Net.

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Qiang Zhong¹, Jinghua Zhang¹, Ying Xu¹, Mengfei Li¹, Baoyun Shen¹, Wenbin Tao¹, Qibin Li¹ - Show less +3 more•Institutions (1)

Guangdong University of Technology¹

27 Feb 2021-Micron

TL;DR: The method proposed in this paper provides a direction for the combination of deep learning technology and micro-CT technology in industrial detection and shows that the segmentation performance superiority of this proposed algorithm and the Dice similarity coefficient reaches 0.843.

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5 citations

Journal Article•DOI•

Fabric defect detection based on transfer learning and improved Faster R-CNN

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Zhao Jia, Zhou Shi, Zheng Quan, Mei Shunqi

01 Jan 2022-Journal of Engineered Fibers and Fabrics

TL;DR: The experimental results show that the detection accuracy and convergence ability of the improved Faster R-CNN are greatly enhanced compared with the current mainstream models, which provides a reference for future fabric defect detection methods.

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Abstract: Fabric defect detection is an important quality inspection process in the textile industry. A fabric defect detection system based on transfer learning and an improved Faster R-CNN is proposed to solve the problems of low detection accuracy, general convergence ability, and poor detection effect for small target defects in existing fabric defect detection algorithms. The pre-trained weights on the big dataset Imagenet are first extracted for transfer learning. Images are then input into the improved Faster R-CNN network, while the ResNet50 and ROI Align are used to replace the original VGG16 feature extraction network structure and a region of interest (ROI) pooling layer to avoid the problems of region mismatch caused by two quantizations from ROI pooling. The region proposal network (RPN) is combined with the multi-scale feature pyramid FPN to generate candidate regions with richer semantic information and project them onto the feature map to obtain the corresponding feature matrix. Cascaded modules are integrated and different IoU thresholds are used for each level to distinguish positive and negative samples. Finally, the softmax classifier is adopted to identify the image and obtain the predictions. The experimental results show that the detection accuracy and convergence ability of the improved Faster R-CNN are greatly enhanced compared with the current mainstream models, which provides a reference for future fabric defect detection methods.

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3 citations

References

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Vladimir Vapnik¹•Institutions (1)

Bell Labs¹

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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

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Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

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"Sub image based eigen fabrics metho..." refers methods in this paper

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Nello Cristianini¹, John Shawe-Taylor²•Institutions (2)

University of Bristol¹, Royal Holloway, University of London²

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TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.

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Abstract: From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

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Proceedings Article•DOI•

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TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.

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11,211 citations