Image-based automated 3D crack detection for post-disaster building assessment

Natural disasters all too often place human lives and property at risk. Recovery efforts following a disaster can be slow and painstaking work, and potentially put responders in harm's way. A system which helps identify defects in critical building elements (e.g., concrete columns) before responders must enter a structure could save lives. In this paper we propose such a system, centered around an image-based three-dimensional (3D) reconstruction method and a new 3D crack detection algorithm. The image-based method is capable of detecting and analyzing surface damages in 3D. We also demonstrate how a robotic platform could be used to gather the set of images from which the reconstruction is created, further reducing the risk to responders. In this regard, image-based 3D reconstructions represent a convenient method of creating 3D models because most robotic platforms can carry a lightweight camera payload. Additionally, the proposed 3D crack detection algorithm also provides the advantage of being able to operate on 3D mesh models regardless of their data collection source. Our experimental results showed that the 3D crack detection algorithm performed well on several sample building elements, successfully identifying cracks, reconstructing 3D profiles, and measuring geometrical characteristics on damaged elements and not finding any cracks on intact ones. The operation and perceived benefits of the proposed method in a post-disaster situation are also discussed in detail.