用近红外高光谱成像和深度学习，实现纺织品纤维的精准分类。
Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning
- 结合近红外高光谱成像与优化卷积神经网络、自编码器模型。
- 在不同纺织结构下均实现稳定分类性能，泛化能力强。
- 适合关注可持续纺织回收与智能分拣的研究者与工程师。
纺织纤维回收对降低纺织业环境影响至关重要。将高光谱近红外（NIR）成像与先进深度学习算法结合，为高效纤维分类与分拣提供了可行方案。本研究探讨了监督与非监督深度学习模型，并测试其在不同纺织结构下的泛化能力。结果表明，经优化的卷积神经网络（CNN）与自编码器网络在多种条件下均表现出稳健的泛化性能。这些成果凸显了高光谱成像与深度学习在推动可持续纺织回收中的潜力，实现准确且鲁棒的纤维分类。
原文摘要 · Abstract (English)
Recycling textile fibers is critical to reducing the environmental impact of the textile industry. Hyperspectral near-infrared (NIR) imaging combined with advanced deep learning algorithms offers a promising solution for efficient fiber classification and sorting. In this study, we investigate supervised and unsupervised deep learning models and test their generalization capabilities on different textile structures. We show that optimized convolutional neural networks (CNNs) and autoencoder networks achieve robust generalization under varying conditions. These results highlight the potential of hyperspectral imaging and deep learning to advance sustainable textile recycling through accurate and robust classification.
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