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使用匹配过器和照片重建的裂检测和量化方法.

Liu Zhen-Liang1, Zhou An1, Ran Xin-Ru1

  • 1School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, Hebei, China.

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概括

本研究介绍了一种使用数字图像处理和无人机数据的自动桥梁裂检测方法. 该方法准确量化了裂,并创建了有效维护桥梁的3D模型.

关键词:
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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
  • 结构健康监测 结构健康监测

背景情况:

  • 桥梁维护依赖于准确的裂检测,但深度学习方法需要广泛的数据集.
  • 传统的数字图像处理提供了可解释性和较低的计算成本,因此需要进一步研究.

研究的目的:

  • 开发使用数字图像处理和无人机参数的桥梁自动裂检测和量化方法.
  • 为改善维护规划,创建桥梁损坏的综合3D模型.

主要方法:

  • 分析无人机收集的桥梁图像.
  • 改进了用于裂细分的匹配过算法.
  • 裂骨提取和长度计算的形态方法.
  • 使用检测结果和无人机飞行参数构建3D模型.

主要成果:

  • 改进的匹配过算法在公共数据集上实现了97.9%的像素精度,72.5%的F1分数和58.1%的交叉点.
  • 该方法成功地应用于桥,错误率仅为2%.
  • 3D模型提供了桥梁损坏的直观表示.

结论:

  • 拟议的数字图像处理方法有效地检测和量化桥梁裂.
  • 与无人机参数的集成使得全面的3D损坏建模能够进行知情决策.
  • 这种方法为桥梁结构健康监测提供了深度学习的实用替代方案.