Overview of fabric defect detection techniques based on computer vision

Huichao Shang; Penglei Li; Xiao Zhang; Xiangqian Peng

doi:10.1117/12.3008027

14 November 2023 Overview of fabric defect detection techniques based on computer vision

Huichao Shang, Penglei Li, Xiao Zhang, Xiangqian Peng

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129340J (2023) https://doi.org/10.1117/12.3008027
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

In modern industrial development and the era of intelligent production, fabric defects have become a crucial aspect of the textile industry. The accurate identification of defects on fabrics through computer vision technology holds significant research value. This article provides an overview of machine vision detection techniques for fabric defects. It classifies and analyzes existing defect detection techniques, summarizes achievements and innovations in recent years, and examines the research progress in defect detection. The article discusses various models and theories in detail, compares commonly used defect detection techniques, and identifies their main shortcomings. It analyzes the research status in the field of defect detection both domestically and internationally in recent years, provides a comprehensive discussion on relevant models and theories, compares them with current common defect detection techniques, highlights the imperfections in mainstream fabric defect detection, and offers predictions for future development trends.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huichao Shang, Penglei Li, Xiao Zhang, and Xiangqian Peng "Overview of fabric defect detection techniques based on computer vision", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129340J (14 November 2023); https://doi.org/10.1117/12.3008027

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