Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning

• URL: http://arxiv.org/abs/2505.03575v1
• Date: Tue, 06 May 2025 14:34:31 GMT
• Title: Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning
• Authors: Maria Kainz, Johannes K. Krondorfer, Malte Jaschik, Maria Jernej, Harald Ganster,
• Abstract summary: Recycling textile fibers is critical to reducing the environmental impact of the textile industry.<n>In this study, we investigate supervised and unsupervised deep learning models and test their generalization capabilities on different textile structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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