Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning
Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in t...
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/506 |
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| author | Mehmet Erdogan Mustafa Dogan |
| author_facet | Mehmet Erdogan Mustafa Dogan |
| author_sort | Mehmet Erdogan |
| collection | DOAJ |
| description | Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches. |
| format | Article |
| id | doaj-art-8af07be406cc4dd7a86e002ab555b9f9 |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-8af07be406cc4dd7a86e002ab555b9f92025-08-20T01:53:42ZengMDPI AGAlgorithms1999-48932024-11-01171150610.3390/a17110506Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep LearningMehmet Erdogan0Mustafa Dogan1Mechatronics Engineering Department, Istanbul Technical University, Maslak Campus, Room 8109, 34467 Maslak, Istanbul, TurkeyControl and Automation Engineering Department, Istanbul Technical University, Maslak Campus, 34467 Maslak, Istanbul, TurkeyQuality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.https://www.mdpi.com/1999-4893/17/11/506machine visionquality controlgabor transformcurvatureintelligent methods |
| spellingShingle | Mehmet Erdogan Mustafa Dogan Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning Algorithms machine vision quality control gabor transform curvature intelligent methods |
| title | Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning |
| title_full | Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning |
| title_fullStr | Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning |
| title_full_unstemmed | Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning |
| title_short | Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning |
| title_sort | enhanced curvature based fabric defect detection a experimental study with gabor transform and deep learning |
| topic | machine vision quality control gabor transform curvature intelligent methods |
| url | https://www.mdpi.com/1999-4893/17/11/506 |
| work_keys_str_mv | AT mehmeterdogan enhancedcurvaturebasedfabricdefectdetectionaexperimentalstudywithgabortransformanddeeplearning AT mustafadogan enhancedcurvaturebasedfabricdefectdetectionaexperimentalstudywithgabortransformanddeeplearning |