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.
Highlights
With rich visual aesthetic characteristics, yarn-dyed plaid fabric (YDPF), interwoven with colored yarns horizontally and vertically, has increasingly attracted consumers’ attention
The classification and search of historical production data for YDPFs do not satisfy the visual needs of sample order production, fabric design, and stock management, which can be solved by image classification
There is no research on the classification of YDPFs, a YDPF image dataset, or the use of convolutional neural networks
Summary
With rich visual aesthetic characteristics, yarn-dyed plaid fabric (YDPF), interwoven with colored yarns horizontally and vertically, has increasingly attracted consumers’ attention. Hundreds of millions of meters of YDPF are produced worldwide annually, including different plaid types. Historical mass-production data have been accumulated for textile production over the years and are useful for sample order production and textile product design. Factories often receive samples from customers that can be efficiently reproduced by visually comparing. CMES, 2022, vol.130, no.3 them with the same or similar products of the past. These abundant plaid patterns inspire textile product designers
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