Abstract: The evenness of yarn is an important parameter for evaluating the quality and texture of yarn. Therefore, the accurate and rapid measurement of yarn evenness occupies a very important position in the entire textile industry. Currently, there are three main techniques used for measuring yarn evenness: capacitive detection, optical detection, and image processing detection. This article describes the basic characteristics of yarn, introduces the evaluation indicators for measuring yarn evenness, and provides a comprehensive overview of the design principles and basic structures of these three different techniques for measuring yarn evenness. Additionally, the article summarizes the research directions of each technique, presents and analyzes the research achievements of these three techniques in different areas, and summarizes their advantages, limitations, and applicable scenarios. Furthermore, considering the current development of yarn evenness measurement techniques, the article explores the future prospects and directions for the development of these three techniques.

    The progress of the textile industry is driving the upgrade and iteration of yarn evenness detection technology. Traditional methods for evenness detection are no longer able to meet the high-precision and high-speed requirements in industrial applications. Among the three main technologies, capacitive detection is the most widely used and applied in mainstream evenness detection instruments. Capacitive detection primarily utilizes air capacitance as a sensing component to obtain the evenness index of the yarn relatively easily. However, this method can introduce errors due to variations in moisture content and blending ratio of the yarn. With the advancement of high-precision optical sensors, optical detection has gained wider application. Optical detection involves projecting a light beam onto the surface of the yarn and using optical sensors to collect corresponding data for analysis, thereby obtaining the evenness of the yarn. This method is susceptible to errors caused by factors such as hairiness and tension. On the other hand, the method based on digital image processing utilizes computers and high-resolution image sensors to perform detection using machine vision. It can provide relatively accurate measurements of yarn evenness and detect yarn defects that may not be easily identified by the previous two methods. However, challenges such as slow detection speed and high system architecture costs currently limit its ability to accurately measure the evenness of high-speed moving yarn.

   The textile industry plays a crucial role in the national economy. As a pillar industry supporting economic and social development, it not only serves as the foundation industry for meeting people's living needs and improving their quality of life but also represents an advantageous industry for international cooperation and integration. In this context, the rapid and accurate assessment of yarn evenness has significant implications for the healthy development of the textile industry. This article analyzes three widely used techniques for measuring yarn evenness: capacitive detection, optical detection, and image processing detection. It summarizes their respective technical characteristics and makes comparisons among them. Each technique has its unique advantages and limitations. Future research should focus on further optimizing these techniques, starting from practical needs, to improve detection speed, precision, accuracy, and stability. It should also address the upcoming technical challenges to achieve more accurate, efficient, and automated measurement of yarn evenness. In addition, it is worth considering the integration of different techniques to form a diversified system for measuring yarn evenness, catering to the needs of different stages in textile production. For example, by leveraging industrial Internet of Things (IoT) technology, an open digital infrastructure can be designed to collect the maximum value from data gathered throughout the production process through machine networking and integration of information flow from the workshop to the cloud. Ultimately, the development and application of these technologies will contribute to improving the quality and efficiency of textile production, driving the overall development of the textile sector.

Key words: yarn evenness, capacitive detection, optical detection, image processing detection, yarn evenness evaluation

摘要： 纱线条干均匀度是评估纱线质量和质地的重要指标，对纺织品质量、性能以及加工工艺有着重要影响，因此准确快速地测定条干均匀度在纺织工业中具有重要意义。通过回顾国内外相关文献，综合讨论了电容式、光电式和图像处理3种条干均匀度检测技术，并对各自的研究进展进行了详细阐述。重点讨论了条干均匀度检测技术的准确度、评价指标和技术要领等，分析了影响条干均匀度检测技术发展的相关因素，并从理论依据、研究方法和应用评价等角度讨论了条干均匀度检测技术目前存在的问题，为纺织工业中的条干均匀度检测提供一定参考，从而促进纺织行业的工业生产和技术进步。

关键词: 条干均匀度, 电容式检测, 光电式检测, 图像处理检测, 纱线均匀度评价