纱线毛羽骨架及长度的跟踪测量

针对现有图像法毛羽测量存在的缺陷,提出了一种用于毛羽分析和长度测量的纱线毛羽骨架跟踪算法。首先以10像素为步长,作纱线条干边缘曲线对毛羽骨架的分割线,得到毛羽起点;接着在毛羽延伸方向上对毛羽起点的上5?邻域点或下5?邻域点进行判断,得到新的毛羽路径点,进行邻域点的重复判断,直到没有毛羽路径点存在,依次记录所有毛羽点生成毛羽路径,并提出了多毛羽路径点和交叉毛羽的解决方案;最后根据2点间的距离计算出毛羽路径中相邻毛羽路径点的像素,从而得到毛羽的测量长度。对长毛羽的跟踪测量和固定分割长度测量的结果显示,毛羽骨架及长度的跟踪测量算法可将测量长度提高24.3%∽666%,测量结果较为精确。     

基于数字图像处理的纱线毛羽检测

为了更准确地检测出纱线毛羽长度及其根数,在结合视频显微镜和图像处理技术的基础上,提出一种新的毛羽检测方法。首先采用MOTIC SME-140视频显微镜采集纱线图像,然后经过灰度变换、图像分割、形态学开运算、图像细化处理,得到完整的纱线条干图像和细化后的毛羽图像,接着以纱线条干边缘为基准线,对毛羽分割点进行判断,最后得出不同长度的毛羽根数。图像法检测结果表明各纱线片段的毛羽根数值较为稳定,并且检测结果与目测图像计数的结果非常接近。因此,本文所提出的毛羽检测方法较现有的光电检测方法更为准确、可靠。     

应用图像处理的纱线黑板毛羽量检测与评价

针对目测法检测纱线黑板毛羽效率低、主观性强等问题,提出一种新的基于图像处理的毛羽检测方法。纱线黑板经扫描仪采集图像,然后进行中值滤波、二值化、形态学运算、局部阈值等处理,得到黑板毛羽图像,统计出毛羽像素点个数,提出评价黑板毛羽量的指标--M指数。采用原料、线密度和纺纱方式各不相同的纱线进行实验,测得18种纱线的毛羽M指数,与乌斯特仪得到的毛羽H值建立回归模型,两种测试结果之间的相关系数为0.975。6种纱线的验证结果表明,本文提出的毛羽检测法和建立的毛羽M指数能较完整地提取和评价整块纱线黑板毛羽,算法精度高、评定结果可信度好。     

交叉毛羽的凹点匹配分割算法

针对纺织行业纱线检测中对于毛羽重叠交叉问题涉及较少,难以准确检测的问题,提出一种基于凹点匹配的方法实现重叠交叉毛羽的分割。首先对纱线图像进行灰度化处理,然后采用形态学方法重建纱线图像,其次利用基于水平集的测地活动轮廓模型(GAC模型)检测纱线毛羽图像的边缘,对边界轮廓进行角点检测,通过设置夹角阈值来寻找纱线毛羽图像轮廓曲线角点中的凹点。最后通过重叠交叉毛羽区域产生的凹点进行匹配构造分割线,对重叠交叉毛羽进行分割。结果表明:该算法对于重叠交叉毛羽的2根分割准确率可达96.8%,对于3～5根重叠交叉毛羽分割准确率可达73.4%。     

基于最大熵与密度聚类相融合的毛羽检测

为能够更加精确地计算出纱线毛羽的根数及毛羽长度,基于最大熵与密度聚类相融合对纱线毛羽的长度及根数进行检测。该方法首先利用双边滤波对采集到的纱线图像进行预处理,滤除图像中的噪声,同时增强纱线毛羽特征;然后利用最大熵对预处理后的纱线图像进行阈值分割,去除条干提取毛羽,并对毛羽进行细化;最后利用密度聚类算法(DBSCAN聚类)对细化后的毛羽进行分类统计,根据所分类的个数以及每类所含像素点的个数计算出毛羽的根数及长度。将实验结果与目测法和基准线法进行比较,结果表明,该方法与目测方法检测的结果非常接近,结果比基准线法更加精确,检测结果准确、有效。     

单一视角下自适应阈值法的纱线毛羽识别及其应用

为更准确测量纱线参数信息,针对图像背景处理和阈值分割算法对纱线图像处理后毛羽信息损失严重的问题,提出自适应灰度增强及线形区域阈值分割算法。并用自制图像采集系统获取6种不同类型的纱线样本,进行图像识别算法的准确性和有效性验证。结果表明:提出的2种算法可明显减少纱线图像信息损失,并且具有良好的鲁棒性,图像法检测的纱线毛羽长度和数量与目测法相近;实现了纱线主体与背景的灰度对比度增强,避免单一阈值导致的图像分割效果差的影响,提高纱线毛羽的识别精度和测量准确性,为后续研究纱线毛羽检测系统提供有效纱线图像分析算法。     

基于机器视觉的玻璃纤维管纱毛羽检测

为实现玻璃纤维管纱毛羽检测的自动化,设计了基于机器视觉的玻璃纤维管纱毛羽检测系统。使用光源、相机、电机等搭建图像获取平台得到毛羽图像;利用Blob连通域分析法进行毛羽区域提取,计算毛羽轮廓矩特征与毛羽区域特征,并结合支持向量机实现毛羽分类;根据毛羽分类结果以及前后2帧图像中毛羽位置坐标差值,对不同类型的毛羽数量进行统计;通过毛羽的最小外接矩形,得到每帧图像中的毛羽长度数据,并取每个毛羽长度数据的最大值作为相应的毛羽长度。结果表明:该系统能够代替人工完成管纱毛羽的有效检测,且单个管纱的检测耗时在10 s以内,能够满足实际的工业需要。     

基于纱线条干边缘曲线切点的纱线毛羽检测

通过对纱线图像进行灰度变换、纱线图像局部阈值分割、纱线分割以毛羽提取等图像处理算法的应用,从而获得精确的纱线条干和纱线毛羽信息,并对纱线毛羽进行细化。实验表明,所提出的毛羽检测方法能够全面、精确地实现对纱线毛羽长度和根数的检测,且对3mm以上的长毛羽的检测精确度更高。     

基于毛羽图像检测的浆纱抗起毛性能评价

为准确检测织造载荷作用前后浆纱的毛羽及有效表征浆纱的抗起毛性能,提出一种基于毛羽量图像检测的浆纱抗起毛性能评价方法.对纯棉14.5 tex的原纱和3种不同上浆率的浆纱,在JN-01浆纱耐磨性能测试仪上模拟织造载荷,并进行不同载荷次数的摩擦起毛实验.起毛后的纱线经图像采集、纱线主干分割、毛羽分割和主摩擦区间定位等处理后,...     

基于多视角图像的纱线三维建模及毛羽测量研究

毛羽是评价纱线外观质量的重要参数之一，但现有的二维测量方法无法描述毛羽空间形态，使得测量结果与实际情况存在一定差别。文章介绍了多视角纱线图像采集装置的构建，对采集的多视角图像进行处理并构建纱线三维模型，对毛羽三维点云进行去噪、细化处理，从而实现对毛羽长度的精确测量。实验结果表明，本方法能有效地获取毛羽的三维信息并准确地测量其长度，与USTER^?TESTER5条干测试仪、ZweigleHL400毛羽测试仪及FZ/T 01086—2020《纺织品纱线毛羽测定方法 投影计数法》标准的测试数据进行比较，进一步验证了这一方法的准确性和实用性。     

Measurement of long yarn hair based on hairiness segmentation and hairiness tracking

For the problem of hairiness information missed in existing hairiness measurement method, the goal of this work is to accurately measure the length of long yarn hairiness and obtain the path over every hairiness point of the whole hairiness. To achieve this goal, the yarn images were captured by the video microscope (MOTIC) and the thinned hairiness images were obtained by a series of image processing. The different measurement baseline and step value were choose to segment long hairiness in the method of hairiness segmentation, and the different hairiness lengths were obtained, the results of length show that the length of 0.5mm (baseline)and 3 pixels (step value) is closest to hairiness real length. And then, the more accurate lengths of the hairiness were calculated by the method of hairiness tracking. The lengths of the two new methods are longer than the length of the method of fixed length (1mm), but the lengths of hairiness tracking is longer than the longest lengths of hairiness segmentation. The compared results show that the method of hairiness tracking can record the information of every point in hairiness and calculate the pixels of a hair fiber, and acquire more complete information of full hairiness then hairiness segmentation.     

A comparative study on yarn hairiness results from manual test and two commercial hairiness metres

The true hairiness (actual hair number and length) of ring, compact and rotor spun yarns was measured by means of a tedious manual method. The hairiness results were then compared with yarn hairiness results obtained from two commercial instruments (Uster tester and Zweigle Hairiness Meter). The comparative analysis between the measurement methods has revealed very significant discrepancy between the true hairiness results and that from commercial instruments, not only just in terms of the number of hairs, but also in terms of the hair-length distribution. The hair numbers obtained from manual method are much greater than that obtained from the hairiness metres, and the true hair-length distribution does not follow the well-known exponential decay. This study shows that the two existing hairiness measuring systems, while essential for rapid assessment of yarn hairiness, are not accurately measuring the true hairiness of spun yarns.     

纱线毛羽的方向性研究

为了利用毛羽方向的特性,有效控制毛羽增长幅度,达到降低毛羽,提高成纱质量的目的,从分析毛羽成因入手,介绍了毛羽方向性的形成原因、纱线毛羽方向的特性,重点研究了毛羽方向与纺纱过程的关系及毛羽方向性在新型纺纱中的特征。指出毛羽方向分为正向毛羽、反向毛羽、不定向毛羽三类,且三者之间可相互转换;毛羽方向与细纱卷绕密度,络筒退绕动程、络筒络纱速度、络纱张力,钢领钢丝圈、细纱至络筒摩擦倒向以及毛羽测试张力等关系密切,且络筒工序毛羽方向改变是毛羽增长的主要原因;毛羽方向性是纱线毛羽所具有的特征,控制络筒毛羽增长实质上就是减少倒向造成毛羽形态改变及方向发生改变后的定向,并不是真正的去除毛羽,利用毛羽的方向特性,可有效地控制毛羽的增长幅度,提高纱线质量。     

Automatic measurement of yarn hairiness based on the improved MRMRF segmentation algorithm

In order to determine the yarn hairiness characteristic index more accurately, a new yarn hairiness testing strategy based on a combination of image acquisition technology is proposed. Firstly, the captured yarn images are processed with gray-scale conversion and skew correction. Secondly, yarn segmentation is implemented using a multi-resolution Markov Random Field (MRMRF) model with a variable weight in the wavelet domain and yarn stem separation is realized through iterative threshold segmentation algorithm. Thirdly, the image of yarn hairiness is extracted. Finally, the total number and actual number of yarn hairiness of different length are counted sequentially based on the segmentation lines and baseline of yarn stem edge. The baseline is obtained by calculating the average distance between yarn stem edge and yarn axis. Furthermore, the feature is analyzed. Experimental results show that, compared with visual observation method, the maximum deviation of proposed image processing algorithm is 3.88%. The proposed approach can make the results of yarn hairiness segmentation more precisely, and then the more reliable results of hairiness feature detection are obtained.     

可移动气圈控制环对不同纱路成纱毛羽的影响

探讨可移动气圈控制环对环锭纺不同纱路成纱毛羽的影响及其机理,并测试和对比分析了可移动气圈控制环对正常纱路和换位纱路成纱毛羽的影响.结果表明,可移动气圈控制环能明显改善不同纺纱方法纺制的纱线毛羽.这不仅肯定了可移动气圈控制环在正常纱路纺纱时能很好的减少纱线表面的毛羽数,还解决了现阶段换位纺纱时气圈形态不稳定的问题.气圈控制环能控制气圈的形状,进而减少因摩擦力和离心力增大所产生的纱线毛羽数,提高纱线的质量.