Feasibility of Genetic Algorithm for Textile Defect Classification Using Neural Network

TL;DR

This paper investigates the use of genetic algorithms to optimize neural networks for textile defect classification, aiming to improve automated inspection accuracy and efficiency in the competitive textile industry.

Contribution

It explores the application of genetic algorithms to tune neural network parameters specifically for textile defect classification, providing empirical insights and performance comparisons.

Findings

Identified effective neural network configurations for defect classification.

Demonstrated improved classification performance with GA-tuned neural networks.

Compared results with existing models to validate effectiveness.

Abstract

The global market for textile industry is highly competitive nowadays. Quality control in production process in textile industry has been a key factor for retaining existence in such competitive market. Automated textile inspection systems are very useful in this respect, because manual inspection is time consuming and not accurate enough. Hence, automated textile inspection systems have been drawing plenty of attention of the researchers of different countries in order to replace manual inspection. Defect detection and defect classification are the two major problems that are posed by the research of automated textile inspection systems. In this paper, we perform an extensive investigation on the applicability of genetic algorithm (GA) in the context of textile defect classification using neural network (NN). We observe the effect of tuning different network parameters and explain the reasons. We empirically find a suitable NN model in the context of textile defect classification. We compare the performance of this model with that of the classification models implemented by others.