A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm
In textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/149 |
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| author | Filipe Pereira Helena Lopes Leandro Pinto Filomena Soares Rosa Vasconcelos José Machado Vítor Carvalho |
| author_facet | Filipe Pereira Helena Lopes Leandro Pinto Filomena Soares Rosa Vasconcelos José Machado Vítor Carvalho |
| author_sort | Filipe Pereira |
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| description | In textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches. |
| format | Article |
| id | doaj-art-7186ead62e184b48b5a0038f60e9c05b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-7186ead62e184b48b5a0038f60e9c05b2025-01-10T13:14:37ZengMDPI AGApplied Sciences2076-34172024-12-0115114910.3390/app15010149A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 AlgorithmFilipe Pereira0Helena Lopes1Leandro Pinto2Filomena Soares3Rosa Vasconcelos4José Machado5Vítor Carvalho6MEtRICs Research Center, University of Minho, Campus of Azurém, 4800-058 Guimarães, PortugalMEtRICs Research Center, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal2Ai, School of Technology, IPCA, 4750-810 Barcelos, PortugalAlgoritmi Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal2C2T Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, PortugalMEtRICs Research Center, University of Minho, Campus of Azurém, 4800-058 Guimarães, PortugalAlgoritmi Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, PortugalIn textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches.https://www.mdpi.com/2076-3417/15/1/149yarn hairinessmechatronic prototypedeep learningYOLOv5 |
| spellingShingle | Filipe Pereira Helena Lopes Leandro Pinto Filomena Soares Rosa Vasconcelos José Machado Vítor Carvalho A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm Applied Sciences yarn hairiness mechatronic prototype deep learning YOLOv5 |
| title | A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm |
| title_full | A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm |
| title_fullStr | A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm |
| title_full_unstemmed | A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm |
| title_short | A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm |
| title_sort | novel deep learning approach for yarn hairiness characterization using an improved yolov5 algorithm |
| topic | yarn hairiness mechatronic prototype deep learning YOLOv5 |
| url | https://www.mdpi.com/2076-3417/15/1/149 |
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