STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Thin Films, Coating Science Technology & Application Review Article

A Comprehensive Study Utilizing Patterned Fabric Images and Detection Techniques with OpenCV and Edge Detection

  1. Rajarao PBV Shri Vishnu Engineering College for Women,

Abstract

This project offers a comprehensive overview of recent advancements in autonomous fabric failure detection methods, a critical aspect of quality control in the textile industry. Detecting flaws in fabric is an increasingly vital automation challenge. The project evaluates the effectiveness of a proposed method by analyzing patterned fabric images featuring typical flaws. The performance of the proposed approach is assessed across various types of fabric flaws. Additionally, the project tests several detection techniques on specific materials, resulting in performance that surpasses expectations. This research holds significant value for scholars and practitioners in image processing and computer vision. It sheds light on the distinctiveness of different defect detection methods, particularly those leveraging OpenCV and Edge detection technologies. By providing insights into the efficacy of these techniques, the project contributes to advancing automation and quality assurance in the textile industry. In summary, the project’s findings underscore the importance of continuous innovation in fabric flaw detection methods. By leveraging cutting-edge technologies and methodologies, such as OpenCV and Edge detection, it offers valuable insights that can enhance both research and practical applications in the field of textile quality control.

Keywords

References (24)

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