Detection of Fiber Defects Using Keypoints and Deep Learning

Siegmund, Dirk; Fu, Biying; José-García, Adán; Salahuddin, A.; Kuijper, Arjan

doi:10.1142/s0218001421500166

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…[Insert Fig. 3] The most frequently used algorithms for fabric defect detection include Principal Component Analysis (PCA) [60], Dictionary Learning [61], Canny operator [62], K-Nearest Neighbor (KNN) [63], Support Vector Machine (SVM) [64], [65], Low-Rank Decomposition [66], and Plain Bayes [67]. Table 5 provides an overview of the traditional machine learningbased methods used for fabric defect detection.…”

Section: The Learning-based Approach To Fabric Defect Detection a Cla...mentioning

confidence: 99%

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Guo,

Liu,

et al. 2024

IEEE Access

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Fabric defect detection is a crucial step of quality control in textile enterprises. The use of computer vision inspection technology in the textile industry is key to achieving intelligent manufacturing. This study sought to determine the progress made and future research directions in intelligent fabric surface defect detection by comprehensively reviewing published literature in terms of algorithms, datasets, and detection systems. Initially, the detection methods are classified as traditional and learning-based methods. The traditional methods are subdivided into model, spectral, statistical, and structural approaches. Learningbased methods are categorized into classical machine learning methods and deep learning methods. The principles, model performance, detection rate, real-time performance, and applicability of deep learning methods are highlighted and compared. In addition, the strengths and weaknesses of all the approaches are elaborated. The use of fabric defect datasets and deep learning frameworks is analyzed. Public datasets and commonly used frameworks are collated and organized. The application of existing fabric inspection systems on the market is outlined. Fabric defect types are systematically named and analyzed. Finally, future research directions are discussed to provide guidance for researchers in related fields.

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Section: The Learning-based Approach To Fabric Defect Detection a Cla...mentioning

confidence: 99%

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Guo,

Liu,

et al. 2024

IEEE Access

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“…The quality of textile products and the efficiency with which they are produced [41] have benefited greatly from the recent application of deep learning approaches to the issue of fabric defect identification [39,40]. However, there are still challenges in using deep learning algorithms within certain sectors despite their efficacy when dealing with segmentation and classification difficulties [42].…”

Section: B Deep Learning Algorithmsmentioning

confidence: 99%

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

Ms Shital K Dhamal, Dr Chandani Joshi, Dr D.S Chouhan

2023

tjjpt

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Regarding maintaining product quality and satisfying customer expectations, fabric defect analysis is vital in textile manufacturing. This research provides a comprehensive assessment of many techniques used for analyzing and pinpointing flaws in standard and Jacquard textiles. This study considers machine vision systems and AI-based algorithms as two examples of cutting-edge automation alongside more conventional human inspection procedures. Manual inspection techniques are extensively reviewed in the first section, with particular emphasis on these techniques' inherent subjectivity and resource inefficiency. More objective and effective defect detection techniques are highlighted as a solution to the problems caused by human bias. The research then looks into automated methods, examining how recent developments in image processing, computer vision, and pattern recognition have the potential to greatly improve the accuracy and speed with which defects may be detected. The major emphasis of this investigation is the use of AI, namely machine learning and deep learning models, for fabric defect identification. This demonstrates how AI may revolutionize textile manufacturing by automating flaw identification and categorization processes. Accuracy, efficiency, scalability, cost-effectiveness, and adaptation to varied fabric compositions, such as basic and complicated Jacquard textiles, are only some aspects considered throughout the evaluation. In addition, the research addresses the problems and opportunities in the field of fabric defect analysis right now. The paper presents prospective improvements, such as hybrid methods and real-time monitoring systems, to solve current constraints and pave the way for a more robust defect analysis framework. These innovations aim to contribute to sustainable practices and customer happiness in the textile manufacturing industry by fostering effective quality monitoring and production optimization. In conclusion, this study provides a thorough comprehension of fabric defect analysis procedures, which is helpful for professionals and academics in the field. The results fuel the never-ending development of quality assurance techniques, resulting in improvements that raise standards, shorten production times, and give the textile business a fighting chance in the market.

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“…Recently, many researchers have applied deep learning techniques to fabric defect detection problems and have achieved satisfying results [72,73] for the improvement of textile product quality and production efficiency [74]. Although deep learning methods have been proved to be powerful when dealing with segmentation and classification problems, there are still some problems in the practical application of specific industries [75].…”

Section: Deep Learning Algorithmsmentioning

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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Detection of Fiber Defects Using Keypoints and Deep Learning

Cited by 6 publications

References 24 publications

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

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

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