Computer Vision based inspection on post-earthquake with UAV synthetic dataset
This addresses the problem of rapid and comprehensive damage assessment after earthquakes for disaster response teams, though it is incremental as it builds on existing deep learning and UAV technologies.
The paper tackles post-earthquake damage detection by developing a pipeline of deep learning models trained on a synthetic dataset and adapted for UAV use, achieving high accuracy in defect detection, component segmentation, and technical condition estimation from a single drone flight.
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.