基于平面镜成像的纱线条干三维合成校准方法

doi:10.13475/j.fzxb.20210403805

摘要/Abstract

摘要：

针对二维图像纱线条干均匀度检测存在信息缺失、纱线条干三维合成准确度不高等问题,在平面镜成像的三维检测系统基础上提出一种纱线条干三维合成校准方法。选用4种不同粗细的纯棉集聚纺纱线,用相机在一幅图像中采集各个纱线的多视角图像,分别用校准方法对xoz平面和xoy平面校准,再进行二值化、形态学开运算处理,得到清晰的纱线条干二值图像,根据平面镜成像系统几何关系合成纱线条干三维模型,计算纱线条干各截面上像素点个数及其变异系数,与Uster TESTER 5测得的二维直径及纱线二维直径CV值对比评价纱线条干建模精度。结果表明,三维模型纱线各截面像素点个数与二维直径相关系数在0.987以上,条干均匀度CV值与Uster法结果的极差在2.36 %以内,证明校准方法可行。

关键词: 纱线条干, 三维模型, 校准方法, 平面镜成像, 图像处理

Abstract:

Aiming at the lack of information in yarn detection from two-dimensional image and at the low accuracy of three-dimensional (3-D) yarn evenness, a calibration method for 3-D yarn evenness based on mirrored image was proposed. Four types of compact cotton yarn with different thickness were selected, and the multi-view images of each yarn were collected in one image by a camera. The collected images were calibrated on the xoz plane and xoy plane, while binarization and morphological opening were carried out respectively to obtain clear binary image of yarn evenness. According to the geometric relationship of the mirror imaging system, the 3-D model of yarn was created, and the number and CV value of white dots on each cross section of yarn were calculated. The modeling accuracy of yarn was evaluated by comparing with the yarn diameter and CV value of yarn diameter experimentally measured by Uster TESTER 5. The results show that the correlation coefficient between the number of pixels in each section of the 3-D yarn model and the diameter is more than 0.987, and the difference of CV value between the proposed method in this research and that from Uster testing is less than 2.36%, which proves the feasibility of the calibration method.

Key words: yarn evenness, three-dimensional model, calibration method, mirrored image, image processing

中图分类号:

TS101.9

马运娇, 王蕾, 潘如如, 高卫东. 基于平面镜成像的纱线条干三维合成校准方法[J]. 纺织学报, 2022, 43(07): 55-59.

MA Yunjiao, WANG Lei, PAN Ruru, GAO Weidong. Calibration method of three-dimensional yarn evenness based on mirrored image[J]. Journal of Textile Research, 2022, 43(07): 55-59.

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参考文献 13

[1]	任志博. 条干均匀度测试仪的发展及其存在的问题研究[J]. 中国纤检, 2010(10):78-80.
	REN Zhibo. Research on the development and existing problems of dry evenness tester[J]. China Fiber Inspection, 2010 (10): 78-80.
[2]	迟开龙, 潘如如, 刘基宏, 等. 基于数字图像处理的纱线条干均匀度检测初探[J]. 纺织学报, 2012, 33(12):19-24.
	CHI Kailong, PAN Ruru, LIU Jihong, et al. Primary discussion on detection of yarn evenness based on digital image processing[J]. Journal of Textile Research, 2012, 33 (12): 19-24.
[3]	章国红, 辛斌杰, 张瑞. 基于图像分析技术的纱线黑板条干检测[J]. 棉纺织技术. 2016, 44(12):19-24.
	ZHANG Guohong, XIN Binjie, ZHANG Rui. Detection of yarn blackboard evenness based on image analysis rechnology[J]. Cotton Textile Technology, 2016, 44(12):19-24.
[4]	张缓缓, 赵妍, 景军锋, 等. 基于亚像素边缘检测的纱线条干均匀度测量[J]. 纺织学报, 2020, 41(5):45-57.
	ZHANG Huanhuan, ZHAO Yan, JING Junfeng, et al. Yarn evenness measurement based on sub-pixel edge detection[J]. Journal of Textile Research, 2020, 41 (5): 45-57.
[5]	李变变, 李忠健, 潘如如, 等. 纱线条干均匀性序列图像测量方法[J]. 纺织学报, 2016, 37(11):26-31.
	LI Bianbian, LI Zhongjian, PAN Ruru, et al. Measurement of yarn evenness using sequence images[J]. Journal of Textile Research, 2016, 37(11):26-31.
[6]	李忠健. 基于图像技术的纱线条干均匀度测量及直观评价方法研究[D]. 无锡: 江南大学, 2019:17-18.
	LI Zhongjian. Research on the measurement and visual evaluation of yarn evenness based on image technology[D]. Wuxi: Jiangnan University, 2019:17-18.
[7]	李忠健, 董龙, 倪海云, 等. 基于散焦信息的纱线毛羽三维测量与验证[J]. 丝绸, 2021, 58(6):41-47.
	LI Zhongjian, DONG Long, NI Haiyun, et al. Three-dimensional measurement and verification of yarn hairiness based on defocus information[J]. Journal of Silk, 2021, 58(6):41-47.
[8]	WANG Lei, XU Bugao, GAO Weidong, et al. Multi-perspective measurement of yarn hairiness using mirrored images[J]. Textile Research Journal, 2016, 86(6):621-629.
[9]	WANG Wendi, XIN Binjie, DENG Na, et al. Objective evaluation on yarn hairiness detection based on multi-view imaging and processing method[J]. Measurement, 2019.DOI: 10.1016/j.measurement.2019.106905. doi: 10.1016/j.measurement.2019.106905.
[10]	LI Guanzhi, et al. AKANKWASA Nicholus Tayari, ZHAO Qiang, A novel system for yarn cross-section analysis based on dual orthogonal CCD sensors[J]. Journal of Natural Fibers, 2019, 16(1): 114-125. doi: 10.1080/15440478.2017.1409151
[11]	WANG Lei, XU Bugao, GAO Weidong. Three-dimensional measurement of yarn hairiness via multiperspective images[J]. Optical Engineering, 2018.DOI: 10.1117/1.OE.57.2.025103. doi: 10.1117/1.OE.57.2.025103.
[12]	李久芳. 基于标准棋盘格的图像校准方法[J]. 电子工业专用设备, 2010, 39(11):4-6.
	LI Jiufang. Image calibration method based on standard checkerboard[J]. Equipment for Electronic Products Manufacturing, 2010, 39 (11): 4-6.
[13]	陆奕辰, 王蕾, 唐千惠, 等. 应用图像处理的纱线黑板毛羽量检测与评价[J]. 纺织学报, 2018, 39(8):144-149.
	LU Yichen, WANG Lei, TANG Qianhui, et al. Detection and evaluation on yarn hairiness of blackboard with image processing[J]. Journal of Textile Research, 2018, 39(8):144-149.

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图像编号	人工测量法	自动测量法
V₁	1.64	1.63
V₂	2.00	1.91
V₃	1.92	1.86
V₄	1.63	1.61
R	1.00	1.00

样品编号	测量方法	像素点个数/像素	直径/mm
1	人工	2 029.85
	自动	1 997.43
	Uster		0.14
2	人工	2 153.01
	自动	2 102.76
	Uster		0.15
3	人工	2 716.32
	自动	2 685.55
	Uster		0.17
4	人工	3 465.35
	自动	3 446.24
	Uster		0.20

样品编号	测量方法	CV值/%	与Uster极差/%
1	人工	12.23	0.93
	自动	11.92	0.62
	Uster	11.30
2	人工	14.45	2.36
	自动	14.22	2.13
	Uster	12.09
3	人工	12.85	1.44
	自动	12.61	1.20
	Uster	11.41
4	人工	16.48	1.12
	自动	16.24	0.88
	Uster	15.36