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<p>This study aims to introduce a cutting-edge method for weave defect detection using Artificial intelligence (AI) and Computer vision (CV) technologies. By employing a Convolutional neural network (CNN) trained on a comprehensive dataset (Python programming codes mentioned in appendix) of textile images with annotated defects, the system automates the identification and classification of fabric irregularities, such as stains and tears. Testing in an industrial setting has shown that this AI-driven approach significantly outperforms traditional manual inspections, enhancing defect detection accuracy and production efficiency. The research highlights the potential of AI and CV to revolutionize Quality control (QC) processes in jute manufacturing industries and beyond.</p>

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Weave defect detection in jute manufacturing value chain using artificial intelligence and computer vision

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • S. M. Vadivel,
  • R. Madhumitha,
  • Yash Nair,
  • P. Manish Aniruddha,
  • S. P. Mithun,
  • Yashwanth Sarathy

摘要

This study aims to introduce a cutting-edge method for weave defect detection using Artificial intelligence (AI) and Computer vision (CV) technologies. By employing a Convolutional neural network (CNN) trained on a comprehensive dataset (Python programming codes mentioned in appendix) of textile images with annotated defects, the system automates the identification and classification of fabric irregularities, such as stains and tears. Testing in an industrial setting has shown that this AI-driven approach significantly outperforms traditional manual inspections, enhancing defect detection accuracy and production efficiency. The research highlights the potential of AI and CV to revolutionize Quality control (QC) processes in jute manufacturing industries and beyond.