This data article presents an online yarn spinning dataset for evaluation and benchmarking of a variety of image processing algorithms and computer vision models for imaging based testing of textile yarn quality. The dataset comprises of continuous yarn spinning videos of 59.05 tex, 29.5 tex and 14.76 tex cotton yarns. These videos were recorded during yarn production on a ring spinning frame using a customised image acquisition system. Three videos of 250 meters yarn length each were recorded for all three yarn varieties. Each yarn spinning video was 29.26 gigabytes in size and contained 20200 image frames. After image acquisition, each yarn sample was physically tested on an industrial yarn quality tester to generate ground truth labels for various yarn quality parameters. The online yarn spinning dataset was recently used to validate computer vision models for online detection of nep like defects in yarn spinning process through a comparison of defect count with ground truth labels [1]. Similarly, in the future, this dataset can be used to evaluate performance of a variety of other imaging based online and offline yarn quality testing and defect detection systems.
Keywords: Image acquisition; Online quality control; Textile technology; Yarn defects; Yarn spinning.
© 2022 The Authors. Published by Elsevier Inc.