Defective and nondefective classif ication of fabric images using shallow and deep networks

Elemmi, Mahantesh C.; Anami, Basavaraj S.; Malvade, Naveen N.

doi:10.1002/int.22774

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…Elemmi et al. 22 designed a simple structure network with the back propagation algorithm to classify defective and nondefective fabrics. For the balance of efficiency and accuracy, Uzen et al.…”

Section: Research On Fabric Defect Detection and Classification By Cnnmentioning

confidence: 99%

“…Similarly, the visual attention mechanism was adopted by Wei et al 21 to reduce the interference of complex textured backgrounds. Elemmi et al 22 designed a simple structure network with the back propagation algorithm to classify defective and nondefective fabrics. For the balance of efficiency and accuracy, Uzen et al 23 extracted deep features from images using the ResNet101 model based on transfer learning and classified the deep features by SVM afterwards, which improved the performance to a certain extent.…”

Section: Research On Fabric Defect Detection and Classification By Cnnmentioning

confidence: 99%

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Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

Jiang

2022

Textile Research Journal

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With the vigorousness of the knitting industry, defect detection and classification of weft-knitted fabrics have become the research fields with extensive application value. However, convolution neural network models suffer from the limitation of convolutional operation, which makes them unable to capture the global features of fabric images abundantly. Although the transformer can compensate for this deficiency, it still has shortcomings such as poor small target recognition and unsatisfactory local information extraction ability. In order sufficiently to actualize the mutual support of relative advantages between the convolution neural network and the transformer, a Swin transformer deformable convolutional network integrated model is proposed in this paper. The Swin transformer deformable convolutional network utilizes the self-attention mechanism with global perception to establish dependencies comprehensively between long-range elements. Meanwhile, the deformable convolution is introduced according to the shaped characteristics of defects to extract local features effectively. Furthermore, a dataset containing 5474 images of weft-knitted fabrics was designed due to the less adequate databases. Experimental results on our weft-knitted fabric dataset and the Irish Longitudinal Study on Ageing (TILDA) database demonstrated that the proposed Swin transformer deformable convolutional network is superior to current state-of-the-art methods and has immense potential in fabric defect detection and classification.

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Section: Research On Fabric Defect Detection and Classification By Cnnmentioning

confidence: 99%

Section: Research On Fabric Defect Detection and Classification By Cnnmentioning

confidence: 99%

Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

Jiang

2022

Textile Research Journal

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“…However, research into AI-based technologies in the textile industry is still in its infancy. This review includes two articles (Elemmi et al, 2022;Yuldoshev et al, 2018) In addition to these papers, five articles focused on implementing Industry 4.0 in the textile and apparel industries. Among these, the results of the Kusi-Sarpong et al…”

Section: Classifications Of Literature On Industry 40 Application In ...mentioning

confidence: 99%

Industry 4.0 in Textile and Apparel Industry: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends

Deepthi

Bansal

2022

Vision: The Journal of Business Perspective

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The manufacturing industry is poised to undergo a paradigm shift with the advent of the Fourth Industrial Revolution, popularly known as ‘Industry 4.0’, which will integrate the physical and digital worlds. As a widely acknowledged phrase among research institutions and universities, the ‘Industry 4.0’ paradigm has attracted significant interest from the academic, business and scientific communities. Even though the concept is not new and has been at the forefront of scholarly research for many years with many interpretations, the ‘Industry 4.0’ concept has just recently been introduced. It is widely accepted not only in the research field but also in the manufacturing ecosystems. However, there is a need to comprehend industry-specific research advancements, trends and gaps due to the diverse applications of different technologies. This article systematically reviews and comprehensively evaluates the research on different Industry 4.0 efforts and their applicability in the textile and manufacturing industries. This article aims to map the current Industry 4.0 literature in the textile and apparel industry to analyse and categorize existing research and identify research gaps. We utilized the PRISMA framework to conduct a systematic review of the literature and 34 research publications on Industry 4.0 in the textile and apparel industry were located using a well-organized keyword search of the Web of Science and Scopus databases. The findings indicate that, of all Industry 4.0 technologies, the internet of things (IoT) and RFID applications are the most extensively investigated in the textile and manufacturing industries. Despite the extensive applications of additive manufacturing (AM)/3D printing and augmented reality (AR), research in both fields is still in its infancy. The study revealed that Germany is the country that has published the most literature on Industry 4.0 projects in the textile and apparel industries. We urge that future academics focus on determining the relevance of Industry 4.0 initiatives to small and medium-sized enterprises (SMEs) as the textile and apparel industry is dominated by SMEs in many developing countries.

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“…Shukla et al Collected the transmission image and reflection image of fabric sample by optical principle, calculated the autocorrelation value of each row and column of the image with the autocorrelation function after preprocessing, and processed and analyzed the transmission image and reflection image respectively to obtain the relevant information of fabric texture parameters [1]. Finally, the fabric structure is determined by the length and weft of each row, and the fabric structure is determined by scanning the length and weft of each row [2]. Raj et al Reduced the gray image level through histogram equalization, then constructed the gray level co-occurrence matrix according to the pixel spacing and angle changes, calculated its eigenvalue, and obtained the fabric density parameter through its periodic calculation [3].…”

Section: Introductionmentioning

confidence: 99%

Fabric yarn detection based on improved fast R-CNN model

Xu¹

2023

3C TIC

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With the rapid development of modern computer technology, and gradually combined with the textile industry, the application of modern computer technology in the field of textile is increasingly extensive, which makes textile production gradually move towards the road of automation development. This paper proposes an automatic detection method of simple weave fabric density based on computer image vision. Computer vision and digital image processing technology are used to analyze and identify the simple weave fabric's warp and weft yarn information and calculate the fabric density. To avoid the phenomenon of warp and weft yarn skew, a method of fabric skew correction based on the Radon transform is proposed. The optimal decomposition order of these four fabrics is k = 2, k = 5, and k = 3. The decomposition series is k. It is found that the relative error of both warp and weft density is about 1.00%. Most of the data obtained by the method of correlation coefficient curve to determine the optimal decomposition series are consistent with the results of the energy curve method. The relative error of the density test results of No. 3 fabric, No. 6 fabric, and No. 7 fabric is higher than 10%, and the relative error of No. 3 fabric is the highest, reaching 66%. This shows serious errors in these three fabrics' warp and weft density. To solve the problems of simple weave fabric density detection, the corresponding algorithm is used to solve the problems. Finally, good results are obtained, which verifies the feasibility of this method. It is significant to realize the automatic measurement of fabric density in textile factories.

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Defective and nondefective classif ication of fabric images using shallow and deep networks

Cited by 7 publications

References 10 publications

Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

Industry 4.0 in Textile and Apparel Industry: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends

Fabric yarn detection based on improved fast R-CNN model

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