Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging
Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies h...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9686715/ |
| _version_ | 1818985797709201408 |
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| author | Mahmoud M. Khodier Sabah M. Ahmed Mohammed Sharaf Sayed |
| author_facet | Mahmoud M. Khodier Sabah M. Ahmed Mohammed Sharaf Sayed |
| author_sort | Mahmoud M. Khodier |
| collection | DOAJ |
| description | Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy. |
| first_indexed | 2024-12-20T18:40:37Z |
| format | Article |
| id | doaj.art-2825e0efe1ab4dac843977a692f1ffee |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T18:40:37Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2825e0efe1ab4dac843977a692f1ffee2022-12-21T19:29:48ZengIEEEIEEE Access2169-35362022-01-0110106531066010.1109/ACCESS.2022.31448439686715Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral ImagingMahmoud M. Khodier0https://orcid.org/0000-0002-1226-6899Sabah M. Ahmed1Mohammed Sharaf Sayed2https://orcid.org/0000-0002-5514-6790Department of Electronics and Communication Engineering, Egypt-Japan University of Science and Technology, New Borg El Arab, Alexandria, EgyptDepartment of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, New Borg El Arab, Alexandria, EgyptDepartment of Electronics and Communication Engineering, Egypt-Japan University of Science and Technology, New Borg El Arab, Alexandria, EgyptManual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy.https://ieeexplore.ieee.org/document/9686715/Complex patternsconvolutional neural networksfabric defect detectionjacquardmultispectral imaging |
| spellingShingle | Mahmoud M. Khodier Sabah M. Ahmed Mohammed Sharaf Sayed Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging IEEE Access Complex patterns convolutional neural networks fabric defect detection jacquard multispectral imaging |
| title | Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging |
| title_full | Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging |
| title_fullStr | Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging |
| title_full_unstemmed | Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging |
| title_short | Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging |
| title_sort | complex pattern jacquard fabrics defect detection using convolutional neural networks and multispectral imaging |
| topic | Complex patterns convolutional neural networks fabric defect detection jacquard multispectral imaging |
| url | https://ieeexplore.ieee.org/document/9686715/ |
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