INTELLIGENT FABRIC DEFECT DETECTION USINGDEEPLEARNINGANDREAL-TIME VISION SYSTEMSAPPLICATION
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| Format: | Recurso digital |
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Zenodo
2025
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| author | U. Satyanarayana, P. Sana Nawaz, P. Charmy, Sk. Md. Sana Gousebee, Sk. Vasiha Thapsum |
| author_facet | U. Satyanarayana, P. Sana Nawaz, P. Charmy, Sk. Md. Sana Gousebee, Sk. Vasiha Thapsum |
| contents | <p><strong>ABSTRACT- </strong>Fabric defect detection plays a vital role in quality control within the textile industry. Computer vision-basedinspection has become a key technology for enabling intelligent manufacturing. This study reviews advancementsin intelligent fabric defect detection, focusing on algorithms, datasets, and detection systems. Detectionmethodsare categorized into traditional and learning-based approaches. Traditional methods include model-based, spectral,statistical, and structural techniques, while learning-based methods are divided into classical machine learninganddeep learning techniques. The study compares deep learning models based on their principles, accuracy, real-timeperformance, and practical applicability. Additionally, it examines commonly used fabric defect datasets and deep learning frameworks, organizingpublicdatasets and widely adopted models. To improve real-time defect detection, the YOLOV8 algorithmis utilized,offering high speed and accuracy in identifying irregularities. YOLO’s efficiency in processing entire imagesinasingle pass makes it well-suited for rapid textile inspection.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17122279 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | INTELLIGENT FABRIC DEFECT DETECTION USINGDEEPLEARNINGANDREAL-TIME VISION SYSTEMSAPPLICATION U. Satyanarayana, P. Sana Nawaz, P. Charmy, Sk. Md. Sana Gousebee, Sk. Vasiha Thapsum <p><strong>ABSTRACT- </strong>Fabric defect detection plays a vital role in quality control within the textile industry. Computer vision-basedinspection has become a key technology for enabling intelligent manufacturing. This study reviews advancementsin intelligent fabric defect detection, focusing on algorithms, datasets, and detection systems. Detectionmethodsare categorized into traditional and learning-based approaches. Traditional methods include model-based, spectral,statistical, and structural techniques, while learning-based methods are divided into classical machine learninganddeep learning techniques. The study compares deep learning models based on their principles, accuracy, real-timeperformance, and practical applicability. Additionally, it examines commonly used fabric defect datasets and deep learning frameworks, organizingpublicdatasets and widely adopted models. To improve real-time defect detection, the YOLOV8 algorithmis utilized,offering high speed and accuracy in identifying irregularities. YOLO’s efficiency in processing entire imagesinasingle pass makes it well-suited for rapid textile inspection.</p> <p> </p> |
| title | INTELLIGENT FABRIC DEFECT DETECTION USINGDEEPLEARNINGANDREAL-TIME VISION SYSTEMSAPPLICATION |
| url | https://doi.org/10.5281/zenodo.17122279 |