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@article{189531,
        author = {RAJESH S and Saruja Lakshmi C and Priyadharshni K and Dharshinee N},
        title = {Fabric Defect Detection using Robust Principal Component Analysis and Deep Learning techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {8240-8250},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189531},
        abstract = {This paper presents a fabric defect detection system using a kernel-based robust principal component analysis (KRPCA)-based non-convex total variation (NTV) with an alternating direction method of multipliers (ADMM) optimization method. The proposed system is based on a KRPCA-NTV model that is used to decompose the input fabric image into two parts: a low-rank and a sparse matrix. The low-rank part contains the normal structure of the fabric, while the sparse part contains the defective regions of the fabric, and CNNs are used to extract discriminative features from the defect regions for classification. In this paper, we propose a novel fabric defect detection method that combines RPCA and CNNs to achieve improved performance in terms of accuracy and efficiency. The experimental results demonstrate that the proposed method outperforms the existing methods in terms of accuracy and efficiency, indicating its potential for practical applications in the textile industry.},
        keywords = {Real-time Image Processing, Computer Vision, KRPCA, low-rank, sparse matrix, Feature Extraction, Automation},
        month = {December},
        }