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Ensemble learning-based CNN for textile fabric defects classification

Xueqing Zhao (Shaanxi Key Laboratory of Clothing Intelligence, National and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Service, School of Computer Science, Xi'an Polytechnic University, Xi'an, China)
Min Zhang (Shaanxi Key Laboratory of Clothing Intelligence, National and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Service, School of Computer Science, Xi'an Polytechnic University, Xi'an, China)
Junjun Zhang (Shaanxi Key Laboratory of Clothing Intelligence, National and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Service, School of Computer Science, Xi'an Polytechnic University, Xi'an, China)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 20 January 2021

Issue publication date: 1 July 2021

Abstract

Purpose

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

Design/methodology/approach

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

Findings

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

Originality/value

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

Keywords

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61806160 and Grant 61806011, in part by the Shaanxi Association for Science and Technology of Colleges and Universities Youth Talent Development Program Grant 20190112.

Citation

Zhao, X., Zhang, M. and Zhang, J. (2021), "Ensemble learning-based CNN for textile fabric defects classification", International Journal of Clothing Science and Technology, Vol. 33 No. 4, pp. 664-678. https://doi.org/10.1108/IJCST-12-2019-0188

Publisher

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Emerald Publishing Limited

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