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.