An Analysis of Fabric Defect Detection Techniques for Textile Industry Quality Control

Abstract

The textile industry has long relied on human inspectors to detect and classify fabric defects, which can be a time-consuming and error-prone process. To address this challenge, an automated and real-time fabric defect-detecting system has been developed that uses machine learning algorithms and advanced image processing techniques to detect and classify various types of fabric defects. The system is capable of capturing high-resolution images of the fabric surface in real time, enabling swift and accurate identification of defects. It has been extensively tested on a wide range of fabric materials and has demonstrated high accuracy rates in defect detection. The automated and real-time nature of the system makes it an ideal tool for quality control in textile production, reducing the need for human inspectors and improving the overall efficiency of the inspection process. This system has the potential to revolutionize the textile industry by improving the quality of textile products while reducing costs and increasing productivity.