A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse
A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse
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DOI：
10.1080/17538947.2021.1966527
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发表时间：
2021-08
影响因子：
5.1
通讯作者：
N. Xu;Debao Huang;Shuang Song;Xiao Ling;Chris Strasbaugh;A. Yilmaz;H. Sezen;R. Qin
中科院分区：
文献类型：
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作者：
N. Xu;Debao Huang;Shuang Song;Xiao Ling;Chris Strasbaugh;A. Yilmaz;H. Sezen;R. Qin
摘要在本文中，我们提供了一个基于无人机的细粒度三维变化检测和建筑物在拆除事件中的连续倒塌性能监测的案例研究。利用拆除不同阶段生成的三维点云，采集多时相斜向摄影测量图像。生成的点云的几何精度已经针对机载和地面激光雷达点云进行了评估，屋顶和立面的平均距离分别为12 cm和16 cm。我们提出了一个层次化的体积变化检测框架，该框架统一了多时相无人机图像，用于姿态估计(不需要地面控制点)、重建和从粗到精的3D密度变化分析。这项工作提供了一种解决方案，能够解决全3D时间序列数据集上的变化检测问题，其中戏剧性的场景内容变化是逐步呈现的。我们对建筑物拆除事件的变化检测结果已与手动标记的地面真实变化进行了评估，并获得了从0.78到0.92的F-1分数，具有始终如一的高精度(0.92-0.99)。拆迁过程中的体积变化是由变化检测得到的，并已被证明能很好地反映建筑物拆迁进程的定性和定量。
ABSTRACT In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event. Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition. The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof and façade respectively. We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation (free of ground control points), reconstruction, and a coarse-to-fine 3D density change analysis. This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively. Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92, with consistently high precision (0.92–0.99). Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.