Fiber Defect Detection of Inhomogeneous Voluminous Textiles

Siegmund, Dirk; Samartzidis, Timotheos; Fu, Biying; Braun, Andreas; Kuijper, Arjan

doi:10.1007/978-3-319-59226-8_27

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Convolution NNs (CNNs) have attracted much attention in many fields such as object detection; in Ref. [72], CNN classification in combination with SURF is used to classify dry-washed textiles such as fiber with defect or without defect. Another classifier used for fabric defect detection is SVM [39].…”

Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

111

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The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.

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Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

111

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“…Table 5 gives details about publications on CNNbased fabric defect detection using manually generated (private) datasets. [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93] The papers investigated were analyzed and summarized in terms of method, classification or number of classes, performance as success, and comparison.…”

Section: Autoencodersmentioning

confidence: 99%

Deep learning-based fabric defect detection: A review

Kahraman

Durmuşoğlu

2022

Textile Research Journal

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The use of the deep learning approach in the textile industry for the purpose of defect detection has become an increasing trend in the past 20 years. The majority of publications have investigated a specific problem in this field. Furthermore, many of published reviews or survey articles preferred to investigate papers from a more general perspective. Compared with published review publications, this study is the first up-to-date study that investigates the implementation of deep learning approaches for the detection of fabric defects from 2003 to the present. As the main objective of this study is to review deep learning-based fabric defect detection, the publications regarding fabric defect detection by using deep learning are examined. The methods, database, performance rates, comparisons, and architecture type of these works were compared with each other. The most widely used deep learning architectures customized deep convolutional neural networks, long short-term memory, generative adversarial networks, and autoencoders. Besides the use of the most used deep learning algorithms, the advantages and disadvantages of these approaches have also been expressed.

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“…In order to obtain better image data and detection results, most existing algorithms require textile to be flattened. erefore, some scholars design defect detection algorithms specifically for textiles with uneven and diverse shapes [118]. is consideration is closer to real-world settings.…”

Section: Real Time Of the Algorithmmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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Fiber Defect Detection of Inhomogeneous Voluminous Textiles

Cited by 11 publications

References 16 publications

A Public Fabric Database for Defect Detection Methods and Results

A Public Fabric Database for Defect Detection Methods and Results

Deep learning-based fabric defect detection: A review

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

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