01406nas a2200181   4500000000100000000000100001008004100002260001200043100002000055700002000075700002200095700002600117700002200143245008300165856003700248490000700285520093200292       2022                            d  c10/20221 aMateusz Żarski1 aBartosz Wójcik1 aJaroslaw Miszczak1 aBartlomiej Blachowski1 aMariusz Ostrowski00aComputer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset  uhttps://arxiv.org/abs/2210.052820 v103 a<div>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.</div>