Open Access

ARTICLE

Deep Neural Network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics

Xiaoting Zhang¹, Weidong Gao^2,*, Ruru Pan²

1 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China
2 College of Textile and Science Engineering, Jiangnan University, Wuxi, 214122, China

* Corresponding Author: Weidong Gao. Email:

(This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)

Computer Modeling in Engineering & Sciences 2022, 130(3), 1533-1546. https://doi.org/10.32604/cmes.2022.018763

Received 16 August 2021; Accepted 27 September 2021; Issue published 30 December 2021

Abstract

Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper reports on the authors’ exhaustive evaluation of the YDPF dataset. With an overall accuracy of 88.78%, CNNs proved to be effective in YDPF image classification. This was true even for the low accuracy of Windowpane fabrics, which often mistakenly includes the Prince of Wales pattern. Image classification of traditional patterns is also improved by utilizing the strip pooling model to extract local detail features and horizontal and vertical directions. The strip pooling model characterizes the horizontal and vertical crisscross patterns of YDPFs with considerable success. The proposed method using the strip pooling model (SPM) improves the classification performance on the YDPF dataset by 2.64% for ResNet18, by 3.66% for VGG16, and by 3.54% for DenseNet121. The results reveal that the SPM significantly improves YDPF classification accuracy and reduces the error rate of Windowpane patterns as well.

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Keywords

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