Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network
To address the challenge of quality measurement in fancy yarns, an improved Pixel Difference Convolution (PDC) for the Pixel Difference Network (PiDiNet) is proposed for measuring the evenness and hairiness of chenille yarn. A computer vision-based image acquisition system is established, utilizing...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-05-01
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| Series: | Journal of Engineered Fibers and Fabrics |
| Online Access: | https://doi.org/10.1177/15589250251338256 |
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| author | Chenghan Yang Yun Xu Jianpeng Zhang Jianxin Zhang |
| author_facet | Chenghan Yang Yun Xu Jianpeng Zhang Jianxin Zhang |
| author_sort | Chenghan Yang |
| collection | DOAJ |
| description | To address the challenge of quality measurement in fancy yarns, an improved Pixel Difference Convolution (PDC) for the Pixel Difference Network (PiDiNet) is proposed for measuring the evenness and hairiness of chenille yarn. A computer vision-based image acquisition system is established, utilizing a backlight source for image capture. The captured chenille yarn images show that most textural features align horizontally, while pile yarn introduces hairiness and fiber ends. Therefore, edge detection via image processing technology is crucial for evenness and hairiness measurement. The improved PDC for PiDiNet is designed to remove hairiness, fiber ends, and other noise, which enhancing horizontal features for edge detection. Evenness is subsequently measured by comparing the detected upper and lower edges of the yarn. Canny edge detection is utilized to detect hairiness and, fiber ends. Hairiness is then measured by subtracting the evenness measurement results from the Canny edge detection results. Experiments comparing the proposed method with other existing methods revealed that the yarn core coefficient of variation (CV%) and hairiness area index (HA) for 10 chenille yarn samples closely match manually measured values, highlighting the efficiency and effectiveness of the proposed method for evenness and hairiness measurement. |
| format | Article |
| id | doaj-art-80912d60b53a4e21af300528b87c92c2 |
| institution | OA Journals |
| issn | 1558-9250 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Engineered Fibers and Fabrics |
| spelling | doaj-art-80912d60b53a4e21af300528b87c92c22025-08-20T02:01:30ZengSAGE PublishingJournal of Engineered Fibers and Fabrics1558-92502025-05-012010.1177/15589250251338256Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference networkChenghan Yang0Yun Xu1Jianpeng Zhang2Jianxin Zhang3School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, ChinaSchool of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, ChinaSchool of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, ChinaTo address the challenge of quality measurement in fancy yarns, an improved Pixel Difference Convolution (PDC) for the Pixel Difference Network (PiDiNet) is proposed for measuring the evenness and hairiness of chenille yarn. A computer vision-based image acquisition system is established, utilizing a backlight source for image capture. The captured chenille yarn images show that most textural features align horizontally, while pile yarn introduces hairiness and fiber ends. Therefore, edge detection via image processing technology is crucial for evenness and hairiness measurement. The improved PDC for PiDiNet is designed to remove hairiness, fiber ends, and other noise, which enhancing horizontal features for edge detection. Evenness is subsequently measured by comparing the detected upper and lower edges of the yarn. Canny edge detection is utilized to detect hairiness and, fiber ends. Hairiness is then measured by subtracting the evenness measurement results from the Canny edge detection results. Experiments comparing the proposed method with other existing methods revealed that the yarn core coefficient of variation (CV%) and hairiness area index (HA) for 10 chenille yarn samples closely match manually measured values, highlighting the efficiency and effectiveness of the proposed method for evenness and hairiness measurement.https://doi.org/10.1177/15589250251338256 |
| spellingShingle | Chenghan Yang Yun Xu Jianpeng Zhang Jianxin Zhang Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network Journal of Engineered Fibers and Fabrics |
| title | Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| title_full | Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| title_fullStr | Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| title_full_unstemmed | Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| title_short | Evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| title_sort | evenness and hairiness measurement of chenille yarn based on an improved pixel difference convolution for the pixel difference network |
| url | https://doi.org/10.1177/15589250251338256 |
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