FDAS: A Knowledge-based Framework for Analysis of Defects in Woven Textile Structures
Abstract
Defects in woven textile structures have been analyzed, and a novel scheme for their classification based on their visual attributes is proposed. The proposed scheme can serve as the underlying framework for a vision-based inspection system. The classification framework has been incorporated in software. The resulting knowledge-based system (FDAS — Fabric Defects Analysis System) identities defects, assigns probable causes for the defects, and suggests plausible remedies to avoid them. The system has been tested with actual fabric defects and has performed well. In addition to being used on the shopfloor, FDAS can be used for training new operators in fabric inspection in weaving and apparel-manufacturing plants.
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For products surface defect detection carried on a conveyor belt, the human eye can detect only 60% of the surface defects, and the width of the product cannot exceed 2 m and the moving speed of the products cannot exceed 30 m/s, in the best case.
“For products surface defect detection carried on a conveyor belt, the human eye can detect only 60% of the surface defects, and the width of the product cannot exceed 2 m and, the moving speed of the products cannot exceed 30 m/s, in the best case [3].”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [3]
Cites
(1986) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-1986-811227 · Cited by 1,933 · see citing works
Hayes-Roth, F. A., WATERMAN, D. A., & Lenat, D. B. (1983) · Andalas University Repository (Andalas University) · Cited by 1,699 · see citing works
Waterman (1986) · Cited by 1,298 · see citing works
Handbook of Textile Testing and Quality Control
Grover, E. B., & Hamby, D. S. (1960) · Cited by 34 · see citing works
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Computer-Vision-Based Fabric Defect Detection: A Survey
Kumar, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.1930.896476 · Cited by 725 · see citing works
Fabric defect detection by Fourier analysis
Chan, C.-H., & Pang, G. K. H. (2000) · IEEE Transactions on Industry Applications · doi:10.1109/28.871274 · Cited by 443 · see citing works
Wan, X., Zhang, X., & Liu, L. (2021) · Applied Sciences · doi:10.3390/app11062606 · Cited by 90 · see citing works
GAN-based Defect Synthesis for Anomaly Detection in Fabrics
Rippel, O., Muller, M., & Merhof, D. (2020) · 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa46521.2020.9212099 · Cited by 22 · see citing works
- Authors
- Srinivasan, K., Dastoor, P. H., Radhakrishnaiah, P., & Jayaraman, S.
- Affiliations
- Georgia Institute of Technology (US)
- ORCID
- Radhakrishnaiah, P.
- Venue
- Journal of the Textile Institute
- Publisher
- Informa UK Limited
- Published
- 1992-01
- Volume
- 83
- Issue
- 3
- Pages
- 431-448
- ISSN
- 0040-5000 (ISSN-L)
- 1754-2340
- OpenAlex
- W2087801281
- Cited by
- 138
- References
- 4
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 7 · 2023: 7 · 2022: 13 · 2021: 15 · 2020: 7 · 2019: 3 · 2018: 6 · 2017: 5 · 2016: 3 · 2015: 5 · 2014: 3 · 2013: 4 · 2012: 8
- FWCI
- 4.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Textile Institute (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Materials Science › Polymers and Plastics › Textile materials and evaluations
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- fabric defects, woven fabrics, defect classification, visual attributes, expert systems, image-based inspection, defect causes, defect correction, operator training, textile inspection
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