TITLE: Computer Vision based inspection on post-earthquake with UAV synthetic dataset 

ABSTRACT: 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.
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.
Computer Vision based inspection on post-earthquake with UAV synthetic dataset
http://arxiv.org/abs/2210.05282v1