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@article{166796,
        author = {Ankita Kadole and Prof. Dr. D. V. Kodavade},
        title = {Fabric Fault Detection using Automated Artificial Intelligence Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2009-2012},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166796},
        abstract = {Fabric defects significantly impact the quality and aesthetics of textile products, leading to production delays and customer dissatisfaction. Traditional manual visual inspection, the primary method for fabric defect analysis, suffers from limitations like subjectivity, time consumption, and inability to keep pace with high-speed production lines. This research investigates the potential of Artificial Intelligence (AI) technologies to address these limitations and enhance fabric defect analysis. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.},
        keywords = {fabric defects, artificial intelligence, defect classifier, AlexNet, deep neural network},
        month = {July},
        }