摘 要

纱线粗细不匀指标是衡量纱线品质的主要指标之一。目前我国测量纱线不匀的方法主要有测长称重法、仪器测量法和目光检测法，这些方法各有其优缺点。随着计算机和图像处理技术的发展，充分利用数字图像处理技术来克服其他检测方法的缺点，提高纱线质量检测水平，就成为学术研究的一个重要课题，对提高纱线质量也具有重要的意义。本文提出了一种利用计算机图像处理和分析技术实现纱线均匀度测试的新方法。

在市场调研的基础上，对现行黑板检验法及其本质，对计算机图像处理检验法的可行性进行了理论分析和研究。本文通过扫描仪采集棉纱黑板图像，然后对采集的图像进行预处理,包括形态滤波、二值化、断点连接。接着对预处理后的图像进行特征提取，得到直径、平均直径、 值、粗细节等判别指标。最后对实验结果进行分析与讨论。

实验表明，用计算机图像处理方法来实现纱线均匀度的测试，具有准确、指标量化等优点，对实现纱线匀度的自动化检测具有一定的应用价值。

关键词：均匀度；图像处理；特征提取；滤波

ABSTRACT

The index of yarn filament irregularity is one of main indices to measure yarn quality. At present, there are mainly three methods to detect yarn irregularity: length measurement and weight measurement method, instruments measurement method and visual measurement method. These methods each have advantages and disadvantages. With the development of computer image processing technique, taking full advantage of digital image processing technique to overcome disadvantages of other detect methods and improving the level of yarn quality detection has become an important research field and has important significance to enhance yarn quality. The paper proposes a new method that makes use of the computer image processing and analysis technique in yarn filament irregularity analysis.

On the basis of careful market-investigaticin, the present Sernplane method and the essence and the feasibility of testing Seripiane through computer were discussed and analyzed. The cotton yarn image was captured by scanning in this paper, then pre-processing the captured image, including morphological filter、binarization、articulation point connection. And then extracting the post-processing image´s feature values, calculating the discriminant index such as yarn diameter、average diameter、CV value、thin and thick node and so on. At last, the experiment results are analyzed and discussed.

The experiments indicate that using computer image processing method to detect yarn quality has the advantage of veracity、quantified indices and has some applied value to realize yarn automatic detection.

Key words：Evenness; Image Processing; Feature Extracting; Filtering

   

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