在线毛度- 环和突出的纤维数据集
Filipe Pereira1,2,3, Leandro Pinto3, Filomena Soares2
1MEtRICs Research Centre, School of Engineering, Guimaraes, Portugal.
Data in brief
|April 8, 2024
概括
一个新的毛发度数据集有助于人工智能开发. 这个资源可以实时检测线缺陷,如循环和突出的纤维,从而推进织工程分析.
科学领域:
- 织工程 织工程 织工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 毛,包括循环和突出的纤维,是织制造业的关键质量参数.
- 准确检测线缺陷对于质量控制和流程优化至关重要.
- 现有的数据集可能缺乏高级AI模型培训所需的多样性和分辨率.
研究的目的:
- 引入一个全面的在线数据集,用于检测毛和相关缺陷.
- 为培训和验证织品质量评估中的人工智能 (AI) 算法提供资源.
- 为了促进自动化线分析和缺陷检测的研究.
主要方法:
- 使用专用系统获取移动线的高分辨率图像 (1.6M像素) (最多4.2米/秒).
- 从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集,从684个原始图像创建一个增强的数据集.
- 包括各种线特征 (例如毛发指数,纤维数,质量参数) 和统计参数 (U,CV,sH).
- 多种格式的数据集可用性 (YOLOv8,COCO JSON,Pascal VOC XML等等) 为了广泛的AI模型兼容性.
主要成果:
- 一个大规模的,注释数据集的紫色棉花线 (100米),以毛发和其他缺陷为特征.
- 通过使用数据集成功验证了人工神经网络模型,用于实时检测线毛发.
- 证明数据集在分析不同属性的实用性,而不仅仅是毛发.
结论:
- 引入的在线数据集是推动人工智能驱动的织品质量控制的宝贵资源.
- 该数据集支持开发可靠的算法,用于实时检测线缺陷.
- 这项工作为未来在自动线分析和织工程方面的研究提供了基础.
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