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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03575 |
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| _version_ | 1866910929691607040 |
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| author | Kainz, Maria Krondorfer, Johannes K. Jaschik, Malte Jernej, Maria Ganster, Harald |
| author_facet | Kainz, Maria Krondorfer, Johannes K. Jaschik, Malte Jernej, Maria Ganster, Harald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning Kainz, Maria Krondorfer, Johannes K. Jaschik, Malte Jernej, Maria Ganster, Harald Computer Vision and Pattern Recognition Applied Physics 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. |
| title | Supervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning |
| topic | Computer Vision and Pattern Recognition Applied Physics |
| url | https://arxiv.org/abs/2505.03575 |