Научная статья на тему '基于图像处理的棉花异纤检测技术与算法'

基于图像处理的棉花异纤检测技术与算法 Текст научной статьи по специальности «Компьютерные и информационные науки»

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Ключевые слова
棉花 / 异纤检测 / 图像处理 / 算法

Аннотация научной статьи по компьютерным и информационным наукам, автор научной работы — 李凯, 吴喜春, 万济滔

在棉花采摘运输加工过程中, 经常混有各式各样的异纤, 会影响后续加工生产的纱线品质, 需要异纤机进行异纤分拣与清除. 由于不同异纤适用的图像处理方法不同, 现对几种异纤检测算法进行分析, 对比几种检测算法之间的优缺点与适用背景, 提出改进异纤检测效果的思路.

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IMAGE PROCESSING BASED COTTON FOREIGN FIBER DETECTION TECHNOLOGY AND ALGORITHM

In the process of cotton picking and transportation, there are often various kinds of foreign fibers mixed in the process, which can affect the quality of yarn produced by subsequent processing, and foreign fiber machines are needed for foreign fiber sorting and removal. Since different image processing methods are applicable to different foreign fibers, several foreign fiber detection algorithms are analyzed to compare the advantages, disadvantages and applicable background of several detection algorithms and propose ideas to improve the effect of foreign fiber detection.

Текст научной работы на тему «基于图像处理的棉花异纤检测技术与算法»

For citation: Li Kai, Wu Xichun, Wan Jitao. Image processing based cotton foreign fiber detection technology and algorithm //

URL: http://rectors.altstu.rU/ru/periodical/archiv/2022/2/articles/2_3.pdf EDN: https://elibrary.ru/mjmfdo

UDK 677.014

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2014,35(05):13-18. DOI: 10.13475/j.fzxb.201405001306. References

[1] Wu M.H., Zhang Y.B. Research and implementation of online detection system for anisotropic fibers in cotton [J]. Science Technology and Engineering, 2016,16(20):212-217.

[2] Liu J. Research on rapid detection system of anisotropic fiber content in cotton [D]. Shandong Agricultural University, 2015.

[3] Meng K. Research on cotton anisotropic fiber detection method [D]. Shaanxi: Xi'an University of Technology, 2013. DOI:1 0.7666/d.Y3107614.

[4] Ren Weijia, Du Yuhong, Zuo Hengli, Yuan Ruwang. Research progress on image segmentation and edge detection methods for anisotropic fiber detection in cotton [J]. Journal of Textiles, 2021, 42(12):196-204.

[5] Wang F., Jin Xiangyu. Analysis of applicability of image recognition method for raw cotton impurities based on edge detection [J]. Modern Textile Technology, 2019, 27 (5):39-43.

[6] Ma YJ, Chen MFL. Shadow elimination method based on improved Laplace-Gaussian operator [J]. Advances in Lasers and Optoelectronics, 2020, 57(12): 105-113.

[7] Li Xiaohui, Chen Zhiyong, Han Longzhi. Research on the application of LoG image segmentation method in cotton fiber inspection [J]. China Fiber Inspection, 2018(01):93-95. DOI: 10.14162/j.cnki.11-4772/t.2018.01.028.

[8] Shi H.Y., Guan S.Q., Wu N. Image multi-resolution differential detection method for heterogeneous fibers in cotton [J]. Journal of Textiles, 2014,35(05):13-18. DOI: 10.13475/j.fzxb.201405001306.

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