VNWoodKnot:用于木结检测和分类的基准图像数据集
Vinh Tran1, Duy Lam1, Tuong Le1
1Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam.
Data in brief
|September 29, 2025
概括
这项研究介绍了VNWoodKnot,这是一个用于检测木材中木材结的新数据集. 该资源有助于开发用于自动木材质量检查的AI模型.
科学领域:
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
- 林业林业 林业 林业 林业
背景情况:
- 木材的缺陷,如结节,显著影响木材的结构完整性和美学.
- 木材行业的自动分类和质量控制需要精确的结节检测.
- 现有的人工智能驱动木材缺陷检查数据集有限.
研究的目的:
- 介绍VNWoodKnot,这是一个新的,高分辨率的图像数据集用于木结检测.
- 为开发和评估木材缺陷检查中的AI模型提供一个基准.
- 促进木材自动化质量控制方面的进步.
主要方法:
- 收集了来自越南工业设施的1515张高分辨率木材表面图像.
- 将图像分类为活结 (519),死结 (496) 和没有结的表面 (500).
- 设计用于图像分类和对象检测任务的数据集.
主要成果:
- 现在VNWoodKnot数据集已经公开了.
- 数据集根据视觉特征区分活结和死结.
- 它解决了基于人工智能的木材缺陷分析资源的严重缺口.
结论:
- VNWoodKnot是木材检查中人工智能发展的重要基准.
- 能够创建实时,可扩展和可靠的深度学习模型.
- 支持木材加工工业中改进的自动分类和质量控制.
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