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A comparison of 2DCNN network architectures and boosting techniques for regression-based textile whiteness estimation
Simulation Modelling Practice and Theory ( IF 4.2 ) Pub Date : 2021-09-04 , DOI: 10.1016/j.simpat.2021.102400
Thanasis Vafeiadis 1 , Nikolaos Kolokas 1 , Nikolaos Dimitriou 1 , Angeliki Zacharaki 1 , Murat Yildirim 2 , Habibe Gülben Selvi 2 , Dimosthenis Ioannidis 1 , Dimitrios Tzovaras 1
Affiliation  

This paper presents a comparative assessment of two-dimensional convolutional neural networks (2DCNN) and boosting methods for regression-based textile whiteness estimation, applied to high resolution images of textiles of an industrial cotton textiles producer, labeled with whiteness values, thus enabling supervised learning. The images were taken under various lighting conditions. Concerning the machine learning methods, Random Forest and XGBoost were the selected and tested boosting techniques on which model hyper-parameter tuning was applied, whereas regarding the 2DCNN architectures, the known from literature ColorNet architecture was selected and a more shallow one, called WERegNet, was introduced. Data augmentation was applied during pre-processing, due to the limited amount of available data. Based on the simulation results, the WERegNet architecture surpasses ColorNet and XGBoost in terms of performance, while it is comparable with Random Forest on test set, based on model selection measure Normalized Root Mean Squared Error.



中文翻译:

基于回归的纺织品白度估计的 2DCNN 网络架构和增强技术的比较

本文介绍了二维卷积神经网络 (2DCNN) 和基于回归的纺织品白度估计的增强方法的比较评估,应用于工业棉纺织品生产商的纺织品的高分辨率图像,标有白度值,从而实现监督学习. 这些图像是在各种光照条件下拍摄的。关于机器学习方法,随机森林和 XGBoost 是选择和测试的增强技术,应用了模型超参数调整,而对于 2DCNN 架构,选择了文献中已知的 ColorNet 架构和更浅的架构,称为 WERegNet,被介绍了。由于可用数据量有限,在预处理期间应用了数据增强。根据仿真结果,

更新日期:2021-09-12
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