纱线毛羽路径匹配追踪检测

针对现有纱线毛羽检测方法无法有效检测弯曲毛羽和交叉毛羽的缺陷,提出一种基于图像法的纱线毛羽路径匹配追踪算法。将采集到的纱线毛羽图像通过预处理、骨干化处理获取毛羽骨干图像,以毛羽端点作为起始点,对其八邻域像素点进行判断获取新的毛羽路径点,重复对毛羽路径点邻域判断直到没有毛羽路径点存在。对毛羽交叉出现多路径点的情况,提出交叉匹配值指标,即根据毛羽交叉点前部分相邻毛羽路径点间斜率并分配动态权重得到毛羽局部斜度,利用交叉匹配值对多路径毛羽点进行匹配获取新的毛羽路径点,通过本文毛羽追踪方法获取毛羽像素数量并转化为毛羽长度。与人工法和投影法检测结果对比表明：本文毛羽追踪检测结果与人工检测毛羽结果误差在4%以内,有效解决了交叉毛羽和弯曲毛羽追踪检测问题,提高了纱线毛羽的检测准确度。     

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。     

基于线性阵列的纱线直径与毛羽测量方法

基于线性阵列测径技术,利用线阵CCD激光传感器研究纱线直径测量和毛羽估计方法,提出了纱线直径线轮廓特征信号的递归平均滤波算法和直径像素坐标获取的线性插值方法。设计了非接触式实时测量系统,并制定实验方案。通过实验仿真验证了递归平均滤波算法,分析了不同浮动阈值对直径像素的影响及子片段毛羽与直径的变化。实验仿真结果证实了纱线直径获取算法的有效性、测量系统的可行性和测量结果的良好再现性,且系统能够精确测量纱线直径和估计毛羽,实现了数据分析过程自动化与实时化,为纺织工艺参数监测提供一定的理论参考和实验数据。     

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。     

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。     

在毛纺过程中产生纱线毛羽的因素分析

在纱线毛羽中,长度为1～4mm的毛羽对织物外观影响较大,成为重点研究对象。叙述了毛羽形成的机理,分析了产生毛羽的多方面原因。提出了减少毛羽的3条主要措施:(1)合理选择原料及其混和比;(2)合理选择工艺流程和设计工艺参数;(3)保持正常的机械运转状态。建议把毛羽数正式列入纱线质量考核指标内。     

G 566新型纱线毛羽测试仪

Zweigle公司的 G566新型毛羽试验仪 ,可能是首次同时在纱线 1 2个长度区间对毛羽计数的测试仪器。此即意味着只需一个单独的试验操作就能有效地测定任何纱线全程范围的毛羽值。在以前的程序中 ,纱线必须分别通过每一长度区间 ,不仅费时 ,而且测试结果不真实 ,因为在多次试验中纱线毛羽是变化的。连续的数据传输也能对所有 1 2个长度区间短片断 ( 2 0 cm或 1 0 0 cm)的毛羽值进行计数 ,并且除了总体毛羽 ,还能揭示整个试验长度的周期性变化。“该设备已很快成为该领域的工业标准 ,”Zweigle公司的 Frank Molfenter说。他加入该设备的销售 ,…     

纱线毛羽测试新工具

<正>纱线毛羽测量水平的显著改善得益于新型测试方法和新型测试设备。瑞士乌斯特公司新近开发的ZWEIGLEHL400型测试设备可比现有的测试设备提高8倍效率,测试精准度无与伦比。精确的纱线毛羽分析已成为纺纱业提高品质的关键。纱线的多毛羽将直接影响纺织品外观和耐     

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.     

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.     

Measurement of yarn hairiness by digital image processing

In their effort to measure yarn hairiness at high speed, the commercially available yarn hairiness testers resort to indirect techniques. Measurement of true length of all hairs can only be done by observing the yarn under a microscope and obtaining a trace of hairs. An attempt was made in this work to automate this task using digital image processing. The challenges were two-fold. The first was development of an algorithm capable of analysing yarn images taken under varying lighting conditions and varying yarn positions. The second was determination of minimum requirement of the image-capturing instrument. Both of these have been reported in this work. A new hairiness index has been proposed and suggested as a better indicator of hairiness than the traditional definition.     

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.     

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。     

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,...     

纱线毛羽图像的二值化处理及其Matlab实现

文章利用CCD扫描来采集纱线灰度图像，运用计算机快速处理数据的能力以及Matlab强大的图像处理功能，采用适当的方法对纱线毛羽图像进行消噪、灰度变换、二值化处理。通过分析二值化图像的特征，分别得出带毛羽纱线和不带毛羽纱线的图像面积，进一步计算出纱线相对毛羽率。结果表明，该方法分割效果好，运算速度快，不需要购买专门的仪器，具有较高的实用价值。