在混合过程中用立体视觉摄像头估计混凝土的可加工性
1Department of Civil Engineering, School of Engineering, Aalto University, 02150 Espoo, Finland.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种机器学习框架,使用立体视觉摄像头来估计混凝土的可行性. 该系统准确地分类了沉降类,为传统的混凝土沉降测试提供了一个透明的替代方案.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 混凝土的可加工性对于放置和压缩至关重要.
- 传统的缩测试是艰苦的,缺乏透明度.
- 需要用于具体可行性估计的自动化方法.
研究的目的:
- 开发一个机器学习框架,以实时估计混凝土条类.
- 使用立体视觉摄像头数据进行自动化可行性评估.
- 为了提供一个透明和有效的替代手动沉测试.
主要方法:
- 一个立体视觉摄像机捕获了混凝土在混凝土机中的深度数据.
- 深度数据被转化为Haralick纹理特征.
- 机器学习分类器,包括XGBoost,接受了培训和评估.
主要成果:
- 在XGBoost分类器实现了0.8179的多类准确性,用于低迷类估计.
- 从混合器叶片和感兴趣区域的选择中提取特征显著提高了准确性和效率.
- 深度数据的删除对分类准确度的影响很小.
结论:
- 拟议的基于立体视觉的机器学习框架为估计具体可行性提供了强大而有竞争力的解决方案.
- 这项技术可以提高混凝土生产监控的透明度和效率.
- 立体视觉为实时混凝土质量控制提供了一种可行的自动化方法.
相关概念视频
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