This paper introduces an online dataset focused on detecting hairiness in yarn, including loop and protruding fibers. The dataset is designed for use in assessing artificial intelligence algorithms. The dataset consists of 684 original images. Through augmentation, this number increases to 1644, with 11,037 annotations derived from videos featuring 56.4tex purple cotton yarn. The videos were captured during the winding and unwinding processes of the purple yarn coil. An image acquisition system capable of capturing high-resolution images while the yarn is in motion was used, reaching speeds of up to 4.2 m/s and producing images with a resolution of 1.6M pixels. This dataset containing 100m of purple cotton yarn images was recorded and is available for download in various formats, including, among others, YOLOv8, YOLOv5, YOLOv7, MT-YOLOv6, COCO JSON, YOLO Darknet, Pascal VOC XML, TFRecord, CreateML JSON. Within an interface developed for a designed mechatronic prototype, users can choose to gather images or videos of yarn. Various characteristics of the yarn, such us: diameter, linear mass, volume, twist orientation, twist step, number of cables, hairiness index, number of loose fibers, thin places, thick places, neps (mass parameters) and U, CV and sH (statistical parameters) can be obtained. Recently, this online yarn spinning dataset was employed to validate artificial neural network models for real-time detection of hairiness in yarns, including loop fibers and protruding fibers. The dataset presented, with its clear annotations and wide array of augmentation techniques, serves as a foundational resource for prospective studies in textile engineering, enabling progress in the analysis and comprehension of yarn analysis.
Keywords: Augmentation; Dataset; Deep learning; Image acquisition; Mechatronic system; Yarn defects; Yarn hairiness.
© 2024 The Authors. Published by Elsevier Inc.