通过混合视觉变压器算法改进混凝土裂检测过程
Mohammad Shahin1, F Frank Chen1, Mazdak Maghanaki1
1Mechanical Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究使用计算机视觉改进了混凝土桥梁检查. 一个定制的卷积神经网络 (CNN) 在裂检测方面实现了超过99%的准确性,在效率方面超过视觉变压器 (ViT).
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 每两年对美国老旧的混凝土桥梁进行检查需要大量的资源.
- 传统的裂检测方法耗时且昂贵.
- 对于高效的桥梁检查技术有着至关重要的需求.
研究的目的:
- 评估计算机视觉模型,以提高混凝土桥梁检查效率.
- 为了比较视觉变压器 (ViT) 和卷积神经网络 (CNN) 模型的裂纹检测性能.
- 评估图像增强算法对检测准确性的影响.
主要方法:
- 应用最先进的视觉变压器 (ViT) 和卷积神经网络 (CNN) 模型.
- 利用了来自Concrete Images for Classification数据集的超过2万张高质量的图像.
- 集成ViT与各种图像增强检测器算法进行基准测试.
主要成果:
- 一个定制的CNN模型在混凝土裂检测中实现了99%以上的准确性.
- 与ViT模型相比,CNN模型的训练时间显著减少.
- 与图像增强探测器相结合的ViT显示了更好的混凝土裂检测精度.
结论:
- 计算机视觉,特别是CNN,为混凝土桥梁裂检测提供了有效的解决方案.
- 这些进步提高了基础设施安全,支持资源节约,并与工业4.0自动化目标保持一致.
- 自动化手动检查降低了成本,并将技术整合到公共基础设施管理中.
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