开发计算机视觉模型,用于对碎石的粒度形状进行分类
Alexey N Beskopylny1, Evgenii M Shcherban'2, Sergey A Stel'makh3
1Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia.
这项研究引入了一种新的神经网络方法,用于分析碎石颗粒形状,改善建筑材料质量评估. 这种机器学习方法通过准确地描述总体形态来增强建筑过程.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 评估建筑材料的传统方法,如粉碎的石头,往往是劳动密集型的,可能缺乏精度.
- 智能算法,特别是机器学习和神经网络,为增强建筑过程提供了先进的功能.
- 描述碎石颗粒形态,特别是状,对于确定其在建筑应用中的适用性至关重要.
研究的目的:
- 开发和评估一种使用3D计算机视觉神经网络来描述压碎石粒形态的新方法.
- 根据它们的3D点云数据,将碎石粒分为形状类别 (针形,板形,立方形).
- 评估机器学习模型在提高建筑材料质量控制的准确性和效率方面的有效性.
主要方法:
- 利用专门设计的3D计算机视觉神经网络,包括基于PointNet和PointCloudTransformer的架构.
- 处理碎石粒的3D图像,以点数据云表示.
- 训练有素的分类算法将谷物形状分类为针状,板状和立方类.
主要成果:
- 在训练PointNet和PointCloudTransformer模型时,获得了0.86的准确性质量指标.
- 证明了神经网络能够准确地分类压碎石粒形态的能力.
- 开发的方法显示了在建筑材料分析中减少手工劳动的潜力.
结论:
- 新的3D计算机视觉神经网络有效地描述了碎石粒形态.
- 基于机器学习的方法为提高建筑材料质量控制提供了一个有希望的途径.
- 这项技术可以补充传统方法,减少人工劳动,并在整个施工阶段验证材料质量.
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