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A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images

机译:一种新的3D构建损伤检测方法,使用多重超级UAV图像

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In this paper, a novel approach is presented that applies multiple overlapping UAV imagesto building damage detection. Traditional building damage detection method focus on 2D changes detection (i.e., those only in image appearance), whereas the 2D information delivered by the images is often not sufficient and accurate when dealing with building damage detection. Therefore the detection of building damage in 3D feature of scenes is desired. The key idea of 3D building damage detection is the 3D Change Detection using 3D point cloud obtained from aerial images through Structure from motion (SFM) techniques. The approach of building damage detection discussed in this paper not only uses the height changes of 3D feature of scene but also utilizes the image's shape and texture feature. Therefore, this method fully combines the 2D and 3D information of the real world to detect the building damage. The results, tested through field study, demonstrate that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and suited well for rapid damage assessment after natural disasters.
机译:本文介绍了一种应用多个重叠的UAV ImageSto建筑损伤检测的新方法。传统建筑物损伤检测方法专注于2D变化检测(即,仅在图像外观中的那些),而在处理建筑物损伤检测时,图像传送的2D信息通常不充分,准确。因此,需要检测在场景的3D特征中的建筑物损坏。 3D构建损伤检测的关键概念是使用从航空图像通过来自运动(SFM)技术的空中图像获得的3D点云进行3D变化检测。本文讨论的建筑物损坏检测方法不仅使用场景的3D特征的高度变化,而且还利用了图像的形状和纹理特征。因此,该方法完全结合了现实世界的2D和3D信息来检测建筑物损坏。通过现场研究测试的结果证明了这种方法在建立损伤检测方面是可行的,有效的。还表明,该方法很容易适用,适用于自然灾害后快速损害评估。

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