Novel approaches for detecting fabric fault using Artificial Neural Network with K-fold validation

A. Andalib, A. Salekin, Mohammad Raihanul Islam, Md. Abdulla-Al-Shami
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引用次数: 2

Abstract

In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we have implemented Artificial Neural Network (ANN). Both of our newly proposed method and improved technique have outperformed the existing methods. We have implemented K-validation to estimate the performance of our classification model. Additionally we have analyzed the performance of our classification model for different experimental parameters. Finally we have presented a comparative analysis of these techniques.
基于K-fold验证的人工神经网络织物故障检测新方法
本文提出了一种基于疵点引起织物图像强度突变的织物疵点检测方法,并建立了疵点分类模型。我们还改进了现有的基于直方图处理的分类器方法。在分类模型上,我们采用了人工神经网络(ANN)。我们提出的新方法和改进的技术都优于现有的方法。我们已经实现了k验证来估计我们的分类模型的性能。此外,我们还分析了我们的分类模型在不同实验参数下的性能。最后对这些技术进行了比较分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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