Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning
This work addresses efficient fiber sorting for the textile recycling industry, representing an incremental improvement in applying existing methods to a specific domain.
The study tackled textile fiber classification for recycling by applying supervised and unsupervised deep learning models to near-infrared hyperspectral imaging data, achieving robust generalization across different textile structures.
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.