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Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recogniti...

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Autores principales: Wang, Rui, Zhang, Zhi-Feng, Yang, Ben, Xi, Hai-Qi, Zhai, Yu-Sheng, Zhang, Rui-Liang, Geng, Li-Jie, Chen, Zhi-Yong, Yang, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181774/
https://www.ncbi.nlm.nih.gov/pubmed/37177618
http://dx.doi.org/10.3390/s23094415
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author Wang, Rui
Zhang, Zhi-Feng
Yang, Ben
Xi, Hai-Qi
Zhai, Yu-Sheng
Zhang, Rui-Liang
Geng, Li-Jie
Chen, Zhi-Yong
Yang, Kun
author_facet Wang, Rui
Zhang, Zhi-Feng
Yang, Ben
Xi, Hai-Qi
Zhai, Yu-Sheng
Zhang, Rui-Liang
Geng, Li-Jie
Chen, Zhi-Yong
Yang, Kun
author_sort Wang, Rui
collection PubMed
description It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.
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spelling pubmed-101817742023-05-13 Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5 Wang, Rui Zhang, Zhi-Feng Yang, Ben Xi, Hai-Qi Zhai, Yu-Sheng Zhang, Rui-Liang Geng, Li-Jie Chen, Zhi-Yong Yang, Kun Sensors (Basel) Article It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection. MDPI 2023-04-30 /pmc/articles/PMC10181774/ /pubmed/37177618 http://dx.doi.org/10.3390/s23094415 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Rui
Zhang, Zhi-Feng
Yang, Ben
Xi, Hai-Qi
Zhai, Yu-Sheng
Zhang, Rui-Liang
Geng, Li-Jie
Chen, Zhi-Yong
Yang, Kun
Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title_full Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title_fullStr Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title_full_unstemmed Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title_short Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
title_sort detection and classification of cotton foreign fibers based on polarization imaging and improved yolov5
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181774/
https://www.ncbi.nlm.nih.gov/pubmed/37177618
http://dx.doi.org/10.3390/s23094415
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