计算机视觉方法用于自动检测混凝土的微观结构缺陷
Alexey N Beskopylny1, Sergey A Stel'makh2, Evgenii M Shcherban'3
1Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
计算机视觉算法,特别是卷积神经网络,可以准确地检测诸如空洞和毛孔之类的具体缺陷. 结合U-Net模型和蜂自动机,实现了高精度和回忆,可靠的结构完整性评估.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
背景情况:
- 人类对混凝土结构的视觉检查在识别空洞和孔隙等缺陷时容易出现错误.
- 计算机视觉,特别是卷积神经网络 (CNN),为建筑结构中缺陷检测提供了可靠的自动化解决方案.
- 准确评估混凝土的完整性对于结构安全和材料质量控制至关重要.
研究的目的:
- 开发和比较使用CNN的计算机视觉算法来识别和分析受损的混凝土部分.
- 评估不同CNN架构 (U-Net,LinkNet,PSPNet) 在具体缺陷细分中的性能.
- 评估自动缺陷检测对质量控制和材料配方调整的有效性.
主要方法:
- 利用U-Net,LinkNet和PSPNet卷积神经网络架构用于对具体缺陷的图像细分.
- 分析了混凝土样本的实验室图像,以评估结构完整性和紧性.
- 监控质量指标,包括精度,回忆,F1分数,IoU (贾卡德指数) 和算法实施期间的准确性.
主要成果:
- 用蜂自动机算法增强的U-Net模型表现出卓越的性能,精度=0.91,回忆=0.90,F1=0.91,IoU=0.84,精度=0.90.
- 开发的细分算法被证明是通用的,在各种成像条件和缺陷大小下有效突出显示缺陷.
- 实现了自动化损坏面积计算和关键/非关键建议.
结论:
- 基于CNN的计算机视觉算法提供了一种强大而准确的方法来检测和分析具体的结构缺陷.
- 带有蜂自动机的U-Net模型为自动化混凝土质量评估提供了高度有效的解决方案.
- 这些自动化工具可以为结构状况评估,混凝土混合设计和生产过程调整提供信息.
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