为了解决纱线条干及毛羽的检测问题,设计了一种基于机器视觉的纱线质量检测系统。该系统主要包括硬件设计和软件检测算法。硬件主要包括图像采集单元、纱线牵引装置和系统控制装置。在软件系统中,利用机器视觉及图像处理方法,分别对纱线条干和纱线毛羽进行检测。利用双边滤波及Otsu阈值实现纱线条干的检测,利用EM算法及密度聚类实现纱线毛羽的检测。将检测结果以及处理后的纱线图像显示在搭建的人工交互界面上,实时显示纱线条干及毛羽的评价。实验结果表明,该系统的实时性及稳定性好,准确率高,具有一定的学术研究价值及应用推广价值。

To address the problems of yarn evenness and hairiness detection, a yarn quality detection system based on machine vision is designed. In addition to hardware, the system core includes a software detection algorithm. The hardware primarily includes an image acquisition unit, a yarn traction device, and a system control device. In terms of software, machine vision and image processing methods were used to detect yarn evenness and yarn hairiness, respectively. Bilateral filtering and Otsu threshold were used to detect the evenness of the yarn, and an EM algorithm and density clustering were used to detect yarn hairiness. The test results and the processed yarn images were displayed in a constructed artificial interaction interface, and the yarn evenness and hairiness were measured and displayed in real time. The experimental results demonstrate that the proposed system delivers effective real-time performance, stability, and high accuracy. In addition to practical applications, the proposed yarn quality detection system is also expected to contribute to academic research in this field.