介绍OpenTextile-NIR:近红外高光谱成像和摄影数据集用于织品的光学识别
Tuomas Sormunen1, Ella Mahlamäki2, Satu-Marja Mäkelä1
1VTT Technical Research Centre of Finland Ltd., Kaitoväylä 1, Oulu 90570, Finland.
Data in brief
|February 24, 2026
概括
这一数据集提供了首个用于织品识别的近红外高光谱成像 (NIR-HSI) 数据. 它支持基于传感器的织品分类和回收研究,使用广泛的光谱和元数据.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 由于材料识别的困难,织品回收面临着挑战.
- 目前用于光学织品识别的现有数据集通常在尺寸或可访问性方面受到限制.
- 先进的传感器技术对于高效和可持续的织废物管理至关重要.
研究的目的:
- 引入第一个开放的近红外超光谱成像 (NIR-HSI) 数据集用于织品识别.
- 支持基于传感器的织品分类和回收的研究.
- 为开发用于织品分析的机器学习算法提供全面的资源.
主要方法:
- 收集了71个后工业织品样本的高光谱图像 (NIR-HSI) 和RGB照片.
- 使用了一个标本SWIR 3高光谱摄像头和一个三星Galaxy A52手机摄像头.
- 组织数据,包括原始/加工图像,光谱数据以及细节化纤维成分和颜色的元数据.
主要成果:
- 该数据集包含来自71个织品样本的1100多万个光谱,其中600多万个有注释,来自71个织品样本.
- 包括高光谱图像,RGB照片,代表性光谱,RGB值和元数据.
- 提供原始和处理的数据,一个例子 Python 脚本可访问性.
结论:
- 这一开放式数据集通过提供大量的,公开可用的资源来解决先前研究的局限性.
- 预计它将加速用于织品回收的光学识别技术的进步.
- 促进各种应用,包括材料分类,纤维含量预测和算法开发.
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