Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics

TL;DR

This paper introduces a Sylvester Matrix Based Similarity Method (SMBSM) for automating fabric defect detection, achieving high accuracy and precision in textile quality control.

Contribution

The paper presents a novel machine vision algorithm utilizing Sylvester matrix similarity for defect detection in textiles, integrating multiple image processing phases.

Findings

Accuracy of 93.4% in defect detection

Precision of 95.8% achieved

Processing speed of 2275 ms

Abstract

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.