Automated Textile Sorting
Using Hyperspectral Imaging
and Deep Learning
Camiel Hoekstra and Pol Rovira, Minor Computer Vision & Data Science
Supervisors: Ben Wolf
Winter 2026
Introduction
Experiments
• Europe will mandate separate textile collection by 2025, creating an urgent
need for fast and accurate textile sorting. Current industrial systems mainly
rely on near-infrared (NIR) sensing, which performs poorly on blended fabrics
such as poly-cotton.
• Hyperspectral imaging (HSI) provides detailed spectral information for every
pixel, enabling improved material discrimination. In this work, we combine HSI
with deep learning to automatically segment textiles from the background
and classify fabric types, including blends. The proposed pipeline aims to
improve recycling efficiency and reduce manual sorting.
• Segmentation performance was evaluated by comparing a U-Net model with fixed and
adaptive thresholding methods. Fixed thresholds were tested at multiple values, and
performance was measured using Intersection over Union (IoU) and inference time.
• For classification, a 1D convolutional neural network was trained on spectral features
extracted from U-Net masks. Three preprocessing methods were evaluated: spectral
averaging, Z-score normalization, and min-max scaling. Models were trained for 50 epochs
with early stopping to limit overfitting.
Abstract
This work presents an automated textile
sorting pipeline using hyperspectral
imaging and deep learning. A U-Net
model segments textile regions, after
which a 1D convolutional neural network
classifies fabrics based on spectral
signatures. Experiments show that deeplearning-based segmentation outperforms
thresholding methods, and Z-score
normalization yields the highest
classification accuracy. The approach
demonstrates strong potential for reliable
textile identification, including blended
fabrics.
Figure 3. Output of the demo program,
prediction is correct. Red bars show the visible
area for the segmentation model
Figure 4. Shows the importance of segmentation
In red with segmentation, Below without
Results
Figure 1. Unet architecture
Materials and Methods
• Hyperspectral data were captured using a Specim FX-17 camera (900–1700
nm, 224 bands) on textile samples. Ground-truth masks were created using
semi-automatic annotation with the Segment Anything Model (SAM).
• Textiles were segmented using a U-Net model to remove background pixels.
Spectral features were extracted from masked regions and classified using a
1D convolutional neural network. Three preprocessing strategies were
evaluated: spectral averaging, Z-score normalization, and min-max scaling.
• The U-Net segmentation model significantly outperformed both fixed and
adaptive thresholding methods, achieving higher Intersection over Union (IoU)
while maintaining comparable inference time. Threshold-based methods were
sensitive to parameter choice and struggled with complex textile shapes.
• For spectral classification, Z-score normalization consistently produced the
highest accuracy with the 1D-CNN. Averaging spectral values performed
moderately well, while min-max normalization showed reduced robustness.
These results highlight the importance of accurate segmentation and proper
spectral preprocessing for reliable textile classification.
Method
IoU (%)
Inference Time (s)
Fixed Threshold
72.1
0.44
Adaptive Threshold
72.8
0.46
U-Net
85.6
0.48
Preprocessing Method
Figure 2. Red bars show where the segmentation model is looking, on the right is the array the 1DCNN is receiving and at the top shows the confidence. The prediction in this case is correct.
Accuracy (%)
Average
81.8
Z-score
90.9
Min-max
72.7
Conclusions
This work demonstrates that combining hyperspectral imaging with deep learning enables accurate and
Acknowledgements
automated textile sorting. U-Net segmentation significantly improves textile isolation compared to threshold-
This project is financially supported by [...] under the [...]
program. / We thank our partners for their valuable input.
achieved the highest accuracy using a 1D-CNN. Despite the limited dataset size, the results show strong
based methods, resulting in cleaner spectral features. For classification, Z-score normalized spectral data
potential for reliable identification of textile materials, including blends, and provide a solid foundation for future
industrial-scale textile recycling systems.
References
https://www.europarl.europa.eu/topics/en/article/20201208STO93327/fast-fashion-eu-laws-for-sustainabletextile-consumption