Classification of Pineapple Fiber Woven Fabrics Based on Convolutional Neural Network

Natthpon Ounyoung, N. Mettripun
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引用次数: 1

Abstract

In this research, we propose a method for classifying 4 types of pineapple fiber woven fabrics which are pure pineapple fiber woven fabrics, manual-connected pure pineapple fiber woven fabrics, machine-connected pure pineapple fiber woven fabrics, and blended pineapple fiber-cotton textile fabric. Each type can refer to the quality and price of these fabrics. The proposed technique is based on transfer learning of the Convolutional Neural Network (CNN). Transfer learning can be separated into 3 steps which are modifying the pretrained network, model training, and assessing the model. The Resnet50 was selected to be our transfer learning network. Finally, the experimental result shows that the classification performance in terms of class accuracy is 95.83 % on average.
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基于卷积神经网络的菠萝纤维机织织物分类
在本研究中,我们提出了菠萝纤维机织物的4种分类方法,分别是纯菠萝纤维机织物、手工连接的纯菠萝纤维机织物、机器连接的纯菠萝纤维机织物和菠萝纤维-棉混纺织物。每种类型都可以参考这些面料的质量和价格。该技术基于卷积神经网络(CNN)的迁移学习。迁移学习可以分为修改预训练网络、模型训练和评估模型3个步骤。我们选择Resnet50作为迁移学习网络。最后,实验结果表明,该方法在分类准确率方面的分类性能平均为95.83%。
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来源期刊
Transactions on Electrical Engineering, Electronics, and Communications
Transactions on Electrical Engineering, Electronics, and Communications Engineering-Electrical and Electronic Engineering
CiteScore
1.60
自引率
0.00%
发文量
45
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