Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics
This work addresses the problem of automating quality control for textile manufacturers by detecting fabric defects, offering an incremental improvement in detection accuracy and speed.
This paper proposes a machine vision system using the Sylvester Matrix Based Similarity Method (SMBSM) to automate defect detection in textile fabrics. The method achieves an accuracy of 93.4% and a precision of 95.8% with a computational speed of 2275 ms.
Fabric defect detection is a crucial quality control step in the textile manufacturing industry. In this article, machine vision system based on the Sylvester Matrix Based Similarity Method (SMBSM) is proposed to automate the defect detection process. The algorithm involves six phases, namely resolution matching, image enhancement using Histogram Specification and Median-Mean Based Sub-Image-Clipped Histogram Equalization, image registration through alignment and hysteresis process, image subtraction, edge detection, and fault detection by means of the rank of the Sylvester matrix. The experimental results demonstrate that the proposed method is robust and yields an accuracy of 93.4%, precision of 95.8%, with 2275 ms computational speed.