Paper Key : IRJ************643
Author: Aditya Vishwas Patil,Prof.p.v.kulkarni,Yash Santosh Shelar,Pratik Sanjay Landge,Syed Abrar Rafiuddin
Date Published: 11 Nov 2024
Abstract
The effectiveness of various machine learning (ML) and deep learning (DL) models in detecting defects in textile fabrics, an essential aspect of ensuring quality control in the textile industry. The study involves the creation of a comprehensive dataset of fabric images with annotated defects, serving as a benchmark for model evaluation. Our findings indicate that while traditional ML models pro- vide satisfactory results for simpler defect types, DL models, par- ticularly CNNs, outperform in detecting complex and subtle defects due to their superior feature extraction capabilities. The research underscores the potential of deep learning in advancing textile defect detection, recommending its integration for improved accuracy and reliability in industrial applications.The research highlights the potential of intelligent fabric detection systems to enhance operational efficiency, reduce costs, and ensure higher standards of prod- uct quality in the textile industry. Future work aims to expand the fabric database, refine the detection algorithms, and explore the ap- plication of this technology in related domains such as apparel man ufacturing and textile recycling.
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