Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed, Aqsa; Zafar, Bushra; Rasheed, Amina; Ali, Nouman; Sajid, Muhammad; Dar, Saadat Hanif; Habib, Usman; Shehryar, Tehmina; Mahmood, Muhammad Tariq

doi:10.1155/2020/8189403

Cited by 116 publications

(55 citation statements)

References 77 publications

(134 reference statements)

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“…A thorough survey [1] is provided of both two-dimensional and three-dimensional surface defect detection systems for various common metal planar material products such as steel, aluminum, copper plates, and strips. The review [79] presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texturebased defect detection, sparse feature based operations, image morphology operations for fabric defect detection. The survey [80] presents a comprehensive survey on surface defect detection technologies over the last two decades for three typical flat steel products of con-casting slabs, rolled steel strips.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

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Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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“…Czimmermann et al presented a detail review based on automatically detection of fabric faults and fabric defect [ 71 ]. Rasheed et al reported a comprehensive study on faults detection methods of textiles [ 72 ]. The widely used detection methods are based on image segmentation, color coordinates, frequency domain, texture-based, image morphology operations and deep learning.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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“…Among the various commonly used evaluation metrics, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and their denotations are discussed in the succeeding section [32]. PPV is the proportion of positive results that are true positive and is also known as precision.…”

Section: Error Matrixmentioning

confidence: 99%

Automated Fabric Defect Detection and Classification: A Deep Learning Approach

Sandhya¹,

Sashikumar²,

Priyanka³

et al. 2021

TLR

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A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With the exiting textile dataset, a maximum classification accuracy of 92.60% is achieved in the conducted simulations. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.

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Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Cited by 116 publications

References 77 publications

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Classification of Textile Polymer Composites: Recent Trends and Challenges

Automated Fabric Defect Detection and Classification: A Deep Learning Approach

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