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Deep Neural Network with Strip Pooling for Image Classification of yarn-dyed plaid fabrics

Computer Modeling in Engineering & Sciences 2022年第3期130卷 1533-1546页

作者： Xiaoting Zhang Weidong Gao Ruru Pan School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China College of Textile and Science Engineering Jiangnan UniversityWuxi214122China

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 *** process does not satisfy the visual needs of sample order production,fabric design,and stock *** study produced an image dataset for YDPFs,collected from 10,661 fabric *** authors believe that the dataset will have significant utility in further research into *** neural networks,such as VGG,ResNet,and DenseNet,with different hyperparameter groups,seemed themost promising tools for the *** paper reports on the authors’exhaustive evaluation of the YDPF *** an overall accuracy of 88.78%,CNNs proved to be effective in YDPF image *** was true even for the low accuracy of Windowpane fabrics,which often mistakenly includes the Prince ofWales *** classification of traditional patterns is also improved by utilizing the strip pooling model to extract local detail features and horizontal and vertical *** strip pooling model characterizes the horizontal and vertical crisscross patterns of YDPFs with considerable *** 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 *** results reveal that the SPM significantly improves YDPF classification accuracy and reduces the error rate of Windowpane patterns as well.

关键词： yarn-dyed plaid fabric image classification image dataset deep neural network strip pooling model

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