UAV Autonomous Inspection and Crack Detection Towards Building Health Monitoring

Abstract:

Aiming at the demands of time-sensitive building health monitoring to promote the automation level of surface disease visual inspection， scene information guided UAV inspection mission planning was proposed. Based on the scene’s prior information， two observation modes， parallel observation and envelope observation， were designed for the structural characteristics of the building， which realized the full coverage obstacle avoidance inspection of the individual building in the narrow space as well as the observation of whole building with millimeter resolution. Meanwhile， a series of effective quantitative indexes for the overall evaluation of the inspection quality were put forward. The facade of the building was divided into 3 720 subregions. The surface cracks were identified and classified by a deep residual network. The result shows that 13 wrong subregions and 14 missing subregions reflect the high accuracy of crack identification. The crack skeletons are mapped to the reconstructed 3D model， which provides data support for the integrated expression of crack morphology and building information. This study combines high-precision 3D reconstruction with surface disease recognition， providing a practical observation and analysis method for integrated building health monitoring.