التفاصيل البيبلوغرافية
| العنوان: |
Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress |
| المؤلفون: |
Peiyao Guo, Yanping Liu, Ying Wu, R. Hugh Gong, Yi Li |
| المصدر: |
IEEE Access, Vol 12, Pp 63777-63808 (2024) |
| بيانات النشر: |
IEEE, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Computer vision inspection, deep learning, fabric defect detection, machine learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Fabric defect detection is a crucial step of quality control in textile enterprises. The use of computer vision inspection technology in the textile industry is key to achieving intelligent manufacturing. This study sought to determine the progress made and future research directions in intelligent fabric surface defect detection by comprehensively reviewing published literature in terms of algorithms, datasets, and detection systems. Initially, the detection methods are classified as traditional and learning-based methods. The traditional methods are subdivided into model, spectral, statistical, and structural approaches. Learning-based methods are categorized into classical machine learning methods and deep learning methods. The principles, model performance, detection rate, real-time performance, and applicability of deep learning methods are highlighted and compared. In addition, the strengths and weaknesses of all the approaches are elaborated. The use of fabric defect datasets and deep learning frameworks is analyzed. Public datasets and commonly used frameworks are collated and organized. The application of existing fabric inspection systems on the market is outlined. Fabric defect types are systematically named and analyzed. Finally, future research directions are discussed to provide guidance for researchers in related fields. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10516319/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3396053 |
| URL الوصول: |
https://doaj.org/article/c833b02aba4444b4be12e806bdbc1a18 |
| رقم الانضمام: |
edsdoj.833b02aba4444b4be12e806bdbc1a18 |
| قاعدة البيانات: |
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