起毛工艺后织物表面绒毛质量的视觉检测方法
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摘要
为了客观评定起毛工艺后织物表面的绒毛质量,提出了基于机器视觉的绒毛质量检测方法。以光切成像原理采集绒毛轮廓图像,利用自适应图像分割方法对绒毛区域进行分割,采用改进的Zernike矩对绒毛与底布边缘进行亚像素定位,并以边缘点进行最小二乘法拟合,将拟合线作为评定绒毛厚度的基准线。通过基准线建立厚度参数模型和间距参数模型,定量描述绒毛表面状态。实验结果表明:该方法在传送速度为20 m/min,采样幅宽700 mm下,能够对织物表面绒毛质量进行客观评价,评定参数与人工检测结果正相关。
In order to evaluate the quality of fluff on fabric surface after fluffing objectively, a method of fluff quality inspection based on machine vision was proposed. The fluff outline image was collected by the principle of light-cut imaging. The adaptive image segmentation method was used to segment the fluff region. The improved Zernike moments were used to sub-pixel localize the edges of the fluff and base cloth, and the edge points were used for least-squares fitting. The fitted line was used as a baseline for assessing fluff thickness. The thickness parameters model and distance parameter model were established by the baseline to quantitatively describe the condition of the surface of the pile. The experimental results show that the method can objectively evaluate the fluff quality on the fabric surface with a transfer speed of 20 m/min and a sampling width of 700 mm. The assessment parameters are positively correlated with the results of manual inspection.
In order to evaluate the quality of fluff on fabric surface after fluffing objectively, a method of fluff quality inspection based on machine vision was proposed. The fluff outline image was collected by the principle of light-cut imaging. The adaptive image segmentation method was used to segment the fluff region. The improved Zernike moments were used to sub-pixel localize the edges of the fluff and base cloth, and the edge points were used for least-squares fitting. The fitted line was used as a baseline for assessing fluff thickness. The thickness parameters model and distance parameter model were established by the baseline to quantitatively describe the condition of the surface of the pile. The experimental results show that the method can objectively evaluate the fluff quality on the fabric surface with a transfer speed of 20 m/min and a sampling width of 700 mm. The assessment parameters are positively correlated with the results of manual inspection.
引文
[1] 孙艳,郭娟琛,杨建忠.毛织物视觉风格研究[J].毛纺科技,2008,36(12):37-40.
[2] 陈慧.毛纺面料特性对服装设计的影响[J]. 毛纺科技,2016,44(8):48-50.
[3] MAK K L, PENG P, YIU K F C. Fabric defect detection using multi-level tuned-matched Gabor filter[J]. Journal of Industrial and Management Optimization,2012,8(2):325-341.
[4] JING J F, ZHANG H H, LI P F, et al. Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method[J]. Journal of the Textile Institute, 2013,104(1):18-27.
[5] 李春雷,高广帅,刘洲峰,等.应用方向梯度直方图和低秩分解的织物疵点检测算法[J].纺织学报,2017,38(3):149-154.
[6] 杨曼,李仁忠,刘阳阳,等.基于改进迭代匹配滤波的织物疵点检测[J].西安工程大学学报,2017,31(3):383-389.
[7] 何峰,周亚同,赵翔宇,等.纹理织物疵点窗口跳步形态学法检测[J].纺织学报,2017,38(10):124-131.
[8] KIM S M, PPAR C K. Evaluation of fabric pilling using hybrid imaging methods[J]. Fibers and Polymers, 2006,7(1):71-89.
[9] 汪亚明,崔新辉,韩永华.基于小波变换及Gabor滤波的起毛起球图像分割[J].丝绸,2016,53(3):37-40.
[10] 周圆圆,潘如如,高卫东,等.基于标准样照与图像分析的织物起毛起球评等方法[J].纺织学报,2010,31(10):29-33.
[11] 杨松林,马帅,丁朝鹏,等. 应用机器视觉的织物表面绒毛率测试系统[J]. 纺织学报,2017,38(6):118-123.
[12] 杨建伟,金德君,卢政大.分数阶的Zernike矩[J].计算机辅助设计与图形学学报,2017,29(3):479-484.
[13] 郭骁,杨冠羽,王征,等.基于Zernike矩和水平集的超声图像分割[J].东南大学学报(自然科学版),2015,45(2):247-250.
[2] 陈慧.毛纺面料特性对服装设计的影响[J]. 毛纺科技,2016,44(8):48-50.
[3] MAK K L, PENG P, YIU K F C. Fabric defect detection using multi-level tuned-matched Gabor filter[J]. Journal of Industrial and Management Optimization,2012,8(2):325-341.
[4] JING J F, ZHANG H H, LI P F, et al. Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method[J]. Journal of the Textile Institute, 2013,104(1):18-27.
[5] 李春雷,高广帅,刘洲峰,等.应用方向梯度直方图和低秩分解的织物疵点检测算法[J].纺织学报,2017,38(3):149-154.
[6] 杨曼,李仁忠,刘阳阳,等.基于改进迭代匹配滤波的织物疵点检测[J].西安工程大学学报,2017,31(3):383-389.
[7] 何峰,周亚同,赵翔宇,等.纹理织物疵点窗口跳步形态学法检测[J].纺织学报,2017,38(10):124-131.
[8] KIM S M, PPAR C K. Evaluation of fabric pilling using hybrid imaging methods[J]. Fibers and Polymers, 2006,7(1):71-89.
[9] 汪亚明,崔新辉,韩永华.基于小波变换及Gabor滤波的起毛起球图像分割[J].丝绸,2016,53(3):37-40.
[10] 周圆圆,潘如如,高卫东,等.基于标准样照与图像分析的织物起毛起球评等方法[J].纺织学报,2010,31(10):29-33.
[11] 杨松林,马帅,丁朝鹏,等. 应用机器视觉的织物表面绒毛率测试系统[J]. 纺织学报,2017,38(6):118-123.
[12] 杨建伟,金德君,卢政大.分数阶的Zernike矩[J].计算机辅助设计与图形学学报,2017,29(3):479-484.
[13] 郭骁,杨冠羽,王征,等.基于Zernike矩和水平集的超声图像分割[J].东南大学学报(自然科学版),2015,45(2):247-250.