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相关实验视频

Updated: Jun 21, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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表面缺陷扩展的BIM生成利用无人机图像和深度学习

Lei Yang1,2, Keju Liu3, Ruisi Ou3

  • 1Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

这项研究引入了一种准确的深度学习方法,用于从无人机图像中检测建筑缺陷. 它将这些缺陷映射到建筑信息模型 (BIM) 上,以改善建筑的数字化.

关键词:
这就是BIM BIM.无人机无人机无人机是什么?深度学习是一种深度学习.表面缺陷检测检测表面缺陷检测纹理映射绘制 纹理映射绘制

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科学领域:

  • 建筑科学 建筑科学
  • 计算机视觉 计算机视觉
  • 建筑领域的数字化

背景情况:

  • 建筑缺陷检查对于建筑数字化至关重要.
  • 无人机和人工智能技术提供先进的检查工具.
  • 将无人机缺陷数据集成到建筑信息建模 (BIM) 中面临着准确性和协调性挑战.

研究的目的:

  • 为无人机图像开发一个准确的缺陷检测方法.
  • 建立一个坐标映射技术,将缺陷数据集成到BIM中.
  • 用表面缺陷信息创建一个丰富的BIM模型.

主要方法:

  • 一种深度学习方法与用于缺陷检测的转移学习相结合.
  • 一种纹理映射方法,将图像坐标转换为BIM项目坐标.
  • 将检测到的缺陷投射到BIM表面,以创建一个表面缺陷扩展的BIM (SDE-BIM).

主要成果:

  • 使用深度学习方法检测缺陷的高精度.
  • 成功地将无人机图像中的缺陷映射到BIM模型.
  • 在南通大学的一项现实实例研究中证明了适用性.

结论:

  • 拟议的方法有效地将基于无人机的缺陷检查数据集成到BIM中.
  • 这种方法增强了现有建筑的数字化.
  • 经过验证的方法在各种建筑检查任务中具有广泛的适用性.