基于反向传播神经网络的混纺纱截面纤维识别

IF 0.6 4区 工程技术 Q4 MATERIALS SCIENCE, TEXTILES AATCC Journal of Research Pub Date : 2021-12-01 DOI:10.14504/ajr.8.s2.19
Zhang Rui, Na Ding, Xinfeng Lu, Yingqi Xu, Bin-jie Xin
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引用次数: 0

摘要

开发了一种智能识别算法来识别含有间位芳纶1313(Nomex)、聚苯-1,3,4-恶二唑(POD)、阻燃粘胶和阻燃乙烯基的混纺纱截面中的纤维。纱线横截面图像是在x400放大倍数下获得的。在Matlab语言的图像处理软件中,使用绘图软件手动分离单根纤维图像,用于训练反向传播(BP)神经网络模型。GrabCut算法用于对图像进行去噪,并将目标与背景分离。最后,通过该程序获得了单纤维图像和纤维分布。结果表明,采用GrabCut算法的BP神经网络模型比传统算法更容易、更准确地识别复杂背景下的纤维类型。
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Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network
An intelligent recognition algorithm was developed to identify fibers in the cross sections of blended yarn containing meta-aramid 1313 (Nomex), poly(phenylene-1,3,4-oxadiazole) (POD), flame resistant viscose, and flame-resistant vinylon. The yarn cross section image was obtained at x400 magnification. Drawing software was used to manually isolate single fiber images for training the back propagation (BP) neural network model in Matlab language image processing software. The GrabCut algorithm was used to de-noise the image and separate the target from the background. Finally, single fiber images and fiber distributions were obtained through the program. The result showed that the BP neural network model with the GrabCut algorithm can identify fiber type in a complex background more easily and more accurately than traditional algorithms.
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来源期刊
AATCC Journal of Research
AATCC Journal of Research MATERIALS SCIENCE, TEXTILES-
CiteScore
1.30
自引率
0.00%
发文量
34
期刊介绍: AATCC Journal of Research. This textile research journal has a broad scope: from advanced materials, fibers, and textile and polymer chemistry, to color science, apparel design, and sustainability. Now indexed by Science Citation Index Extended (SCIE) and discoverable in the Clarivate Analytics Web of Science Core Collection! The Journal’s impact factor is available in Journal Citation Reports.
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