#5675. A computer vision based online quality control system for textile yarns

August 2026publication date
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Engineering (all);
Computer Science (all);
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Abstract:
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.
Keywords:
Computer vision; Image acquisition; Online quality control; Textile yarn; Viola-Jones algorithm; Yarn spinning

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