期刊
SIMULATION MODELLING PRACTICE AND THEORY
卷 114, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.simpat.2021.102400
关键词
Industry 4; 0; 2DCNN; Regression; Machine learning; Random Forest; XGBoost; Deep learning; Textile whiteness
资金
- European Commission [HORIZON 2020-INNOVATION ACTIONS (IA)-869884-RECLAIM]
This study presents a comparative assessment of 2DCNN and boosting methods for textile whiteness estimation, with WERegNet architecture outperforming ColorNet and XGBoost in terms of performance while being comparable to Random Forest.
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.
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