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Image-based retrieval of concrete crack properties for bridge inspection

机译:基于图像的混凝土裂缝特性检索,用于桥梁检查

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摘要

Cracking can invite sudden failures of concrete structures. The objective of this research is to develop an integrated model based on digital image processing in developing the numerical representation of defects. The integration model consists of crack quantification, change detection, neural networks, and 3D visualization models to visualize the defects in such a way that it mimics the on-site visual inspections. The crack quantification model evaluates crack lengths based on the perimeter of the skeleton of a crack which considers the tortuosity of the crack. The change detection model is based on the Fourier Transform of digital images eliminating the need for image registration as required in the traditional. Also, the integrated model as proposed here for crack length and change detection is supported by neural networks to predict crack depth and 3D visualization of crack patterns considering crack density as a key attribute.
机译:开裂会引起混凝土结构的突然破坏。这项研究的目的是开发一种基于数字图像处理的集成模型,以开发缺陷的数值表示。集成模型由裂纹量化,变化检测,神经网络和3D可视化模型组成,以可视化缺陷的方式可视化缺陷,从而模仿了现场视觉检查。裂纹量化模型基于考虑裂纹曲折性的裂纹骨架的周长评估裂纹长​​度。更改检测模型基于数字图像的傅立叶变换,从而消除了传统图像中对图像配准的需求。同样,这里提出的用于裂缝长度和变化检测的集成模型由神经网络支持,以将裂缝密度作为关键属性来预测裂缝深度和裂缝模式的3D可视化。

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