An intelligent method for NIR-based prediction of cashmere fiber and wool fiber using Markov transition field and improved YOLOv8
Predicting cashmere fiber and wool fiber is one of the main challenges in the textile industry. Near infrared spectroscopy(NIR) is a fast, nondestructive and quickly packaged detection method. Due to the highly similar characteristics of the near infrared spectroscopy of cashmere fiber and wool fibe...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-07-01
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| Series: | Journal of Engineered Fibers and Fabrics |
| Online Access: | https://doi.org/10.1177/15589250251355354 |
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| Summary: | Predicting cashmere fiber and wool fiber is one of the main challenges in the textile industry. Near infrared spectroscopy(NIR) is a fast, nondestructive and quickly packaged detection method. Due to the highly similar characteristics of the near infrared spectroscopy of cashmere fiber and wool fiber, it is difficult to distinguish them. In order to improve the accuracy of predicting cashmere fiber and wool fiber, a near infrared spectroscopy cashmere fiber and wool fiber prediction model based on Markov transition field (MTF) and improved YOLOv8 is proposed in this paper. This method calculates the Markov transition matrix of local near infrared spectroscopy data between adjacent wavelength intervals, arranges each probability in wavelength order to expand the Markov transition matrix, forming a MTF of local wavelengths. By replacing the backbone network of YOLOv8 with a hierarchical visual transformer using displacement windows, the network’s attention to local frequency bands and peaks is enhanced. Dropout is added to Swin Transformer (ST) to prevent network overfitting. To examine the effectiveness and stability of the model, it is compared with KNN, decision trees, random forests, AlexNet, VGG16, GoogLeNet, ResNet50, YOLOv8 and other models, and ablation experiments are conducted to further validate the proposed model structure. Experimental results show that the average prediction accuracy of cashmere fiber and wool fiber using this method is highest at 97.01%. The proposed near infrared spectroscopy cashmere fiber and wool fiber prediction model based on MTF and improved YOLOv8 can achieve rapid and non-destructive prediction of cashmere fiber and wool fiber, providing new ideas for qualitative analysis in the field of near infrared spectroscopy. |
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| ISSN: | 1558-9250 |