Yarn quality control is a crucial step in producing high quality textile end products. Online yarn testing can reduce latency in necessary process control by providing rapid insights into yarn quality, leading to production of superior quality yarns. However, both widely used capacitance based evenness testers and emerging imaging based evenness testing systems are largely offline in operation (i.e. a posteriori). A suitable online system that could be employed to test quality of a variety of yarns in normal industrial processing conditions does not yet exist.

In this study, we propose an online evenness testing system for measurement of a certain type of yarn defect called nep by using imaging and computer vision techniques. The developed system directly captures yarn images on a spinning frame and uses Viola-Jones object detection algorithm for real-time detection of nep defects. The validation of nep detection algorithms and comparison of the new method with an existing evenness tester in terms of nep count demonstrated its reasonable defect detection accuracy and promising potential for application in wider yarn spinning industry.

纱线质量控制是生产高质量纺织品最终产品的关键步骤。在线纱线测试可以通过提供对纱线质量的快速洞察来减少必要过程控制的延迟，从而生产出优质的纱线。然而，广泛使用的基于电容的均匀度测试仪和新兴的基于成像的均匀度测试系统都在很大程度上离线运行（即后验）。目前尚不存在可用于在正常工业加工条件下测试各种纱线质量的合适在线系统。

在这项研究中，我们提出了一种在线均匀度测试系统，用于通过使用成像和计算机视觉技术来测量称为棉结的某种类型的纱疵。开发的系统直接捕获细纱机上的纱线图像，并使用 Viola-Jones 对象检测算法实时检测棉结缺陷。棉结检测算法的验证以及新方法与现有条干测试仪在棉结数方面的比较证明了其合理的缺陷检测精度和在更广泛的纱线纺纱行业应用的潜力。