Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network

Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional...

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Main Authors: Rongfei Xia, Yifei Chen, Yangfeng Ji
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Advances in Polymer Technology
Online Access:http://dx.doi.org/10.1155/2024/5926658
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author Rongfei Xia
Yifei Chen
Yangfeng Ji
author_facet Rongfei Xia
Yifei Chen
Yangfeng Ji
author_sort Rongfei Xia
collection DOAJ
description Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal detection rates for testing dirty marks, clip marks, broken yams, and defect-free were 88.01%, 90.15%, 98.01%, and 97.73%, respectively. The experimental results show that the proposed method is effective, feasible, and has significant potential for fabric defect detection.
format Article
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institution Kabale University
issn 1098-2329
language English
publishDate 2024-01-01
publisher Wiley
record_format Article
series Advances in Polymer Technology
spelling doaj-art-68c51dda1f60471fb7818480f21c20e62025-02-03T07:23:41ZengWileyAdvances in Polymer Technology1098-23292024-01-01202410.1155/2024/5926658Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural NetworkRongfei Xia0Yifei Chen1Yangfeng Ji2Chengyi CollegeSchool of Marine EngineeringChengyi CollegeAutomatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal detection rates for testing dirty marks, clip marks, broken yams, and defect-free were 88.01%, 90.15%, 98.01%, and 97.73%, respectively. The experimental results show that the proposed method is effective, feasible, and has significant potential for fabric defect detection.http://dx.doi.org/10.1155/2024/5926658
spellingShingle Rongfei Xia
Yifei Chen
Yangfeng Ji
Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
Advances in Polymer Technology
title Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
title_full Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
title_fullStr Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
title_full_unstemmed Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
title_short Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network
title_sort detection of microdefects in fabric with multifarious patterns and colors using deep convolutional neural network
url http://dx.doi.org/10.1155/2024/5926658
work_keys_str_mv AT rongfeixia detectionofmicrodefectsinfabricwithmultifariouspatternsandcolorsusingdeepconvolutionalneuralnetwork
AT yifeichen detectionofmicrodefectsinfabricwithmultifariouspatternsandcolorsusingdeepconvolutionalneuralnetwork
AT yangfengji detectionofmicrodefectsinfabricwithmultifariouspatternsandcolorsusingdeepconvolutionalneuralnetwork