This paper addresses the problem of crack detection which is essential for health monitoring of built infrastructure. Our approach includes two stages, data collection using unmanned aerial vehicles (UAVs) and crack detection using histogram analysis. For the data collection, a 3D model of the structure is first created by using laser scanners. Based on the model, geometric properties are extracted to generate way points necessary for navigating the UAV to take images of the structure. Then, our next step is to stick together those obtained images from the overlapped field of view. The resulting image is then clustered by histogram analysis and peak detection. Potential cracks are finally identified by using locally adaptive thresholds. The whole process is automatically carried out so that the inspection time is significantly improved while safety hazards can be minimised. A prototypical system has been developed for evaluation and experimental results are included.
More recently, Phung et al. REF presented a crack detection method using histogram analysis; unmanned aerial vehicles were used to collect the data, and then they built a 3D model of the structure from laser scanners for using in clustering histogram analysis and peak detection.
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
ArXiv
ArXiv
Engineering Computer Science