用计算机视觉探测木材的复杂性:从像素到属性
Mirko Lukovic1, Laure Ciernik2, Gauthier Müller1
1Laboratory for Cellulose & Wood Materials, WoodTec Group, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Journal of the Royal Society, Interface
|April 16, 2024
概括
机器学习模型可以从挪威杉木表面的彩色图像中预测木材强度. 这种计算机视觉方法提供了一种可行的方法,用于自动分类木板.
科学领域:
- 林业林业 林业 林业 林业
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
背景情况:
- 工业木材分类依赖于手工评估或专用机器.
- 在建筑应用中,精确地分类木材板块至关重要.
- 木材的视觉特征,就像纤维图案一样,与强度属性相关.
研究的目的:
- 开发一种机器学习模型,用彩色图像来预测木板状板块的强度特性.
- 为了研究预先训练的ResNet50模型对木材分类的有效性.
- 评估计算机视觉在自动化木材叶片分类中的潜力.
主要方法:
- 利用工业木材分级机器的数据进行培训.
- 采用预先训练的ResNet50模型,通过超过15,000张挪威杉的高清图像进行了微调.
- 应用数据增强技术以提高模型性能.
主要成果:
- 在预测强度相关性质时,达到超过0.9的确定系数 (R2) 值.
- 证明了模型能够将视觉表面特征与木材机械性能相关联的能力.
- 通过使用彩色成像数据成功训练了一种机器学习模型.
结论:
- 计算机视觉,由机器学习驱动,为自动木板层分类提供了一个有前途的解决方案.
- 开发的模型显示高准确度预测木材强度从视觉数据.
- 这项技术可以通过高效的分类来满足对建筑级木材日益增长的需求.
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