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A computer vision based online quality control system for textile yarns

•Yarn quality is tested live on ring spinning frame using a new method based on imaging and computer vision techniques.•Ultra-low exposure time imaging adequately captured images of highly dynamic yarns during production on a spinning frame.•Nep like defects were effectively detected by using Viola-...

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Bibliographic Details
Published in:Computers in industry 2021-12, Vol.133, p.103550, Article 103550
Main Authors: Haleem, Noman, Bustreo, Matteo, Del Bue, Alessio
Format: Article
Language:English
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Summary:•Yarn quality is tested live on ring spinning frame using a new method based on imaging and computer vision techniques.•Ultra-low exposure time imaging adequately captured images of highly dynamic yarns during production on a spinning frame.•Nep like defects were effectively detected by using Viola-Jones algorithm in live yarn images.•The nep count reported by imaging based detection system was substantially higher than state of the art Uster evenness tester. 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.
ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2021.103550