Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Han, Youngjoo; Yu, Ha-Jin

doi:10.3390/app10072511

Cited by 27 publications

(18 citation statements)

References 17 publications

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“…The particular DM method is often chosen with respect to the nature of the data and according to the information type intended to be mined. As Han & Kamber (2001) mentioned -the kind of knowledge to be mined determines the data mining functions to be performed. This statement is also supported by Gibert et al (2010), who believe that the choice of the DM method used depends on both goals of the problem to be solved and the structure of the available data set.…”

Section: Overview Of the Dm Methodsmentioning

confidence: 99%

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová

Bína

Váchová

2022

Prod.

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Paper aims: This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations.Originality: Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing.Research method: In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings:The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice:This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

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Section: Overview Of the Dm Methodsmentioning

confidence: 99%

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová

Bína

Váchová

2022

Prod.

View full text Add to dashboard Cite

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“…Han et al [8] proposed defect detection method by using the stacked convolutional autoencoders. The autoencoders were trained on non-defected data and synthetic defected data by using expert-based knowledge of defect characteristics.…”

Section: Defect Detection Methods With Data Augmentationmentioning

confidence: 99%

A sight on defect detection methods for imbalanced industrial data

Chaabi

Hamlich

2022

ITM Web Conf.

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Product defect detection is a challenging task, especially in situations where is difficult and costly to collect defect samples. Which make it quite difficult to apply supervised algorithms as their performances decrease by training the model on imbalanced data. To tackle this problem, researchers used data augmentation and one-class classification to detect defects in industrial areas. In this paper, we list defect detection applications for imbalanced industrial data and we report the benefits and limitation of those methods.

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“…erefore, many researchers employ semisupervised and unsupervised learning algorithms for the detection [115]. In addition, some studies utilize nondefect image data and synthetic defective image data generated by using defect characteristics based on expert knowledge [91]. Chen et al [116] propose a data augmentation method based on automatic image acquisition.…”

Section: Datasetmentioning

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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Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Cited by 27 publications

References 17 publications

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

A sight on defect detection methods for imbalanced industrial data

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

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