TY - JOUR
AU - Mateusz Żarski
AU - Bartosz Wójcik
AU - Jaroslaw Miszczak
AU - Bartlomiej Blachowski
AU - Mariusz Ostrowski
AB - <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>

BT - IEEE Access
DA - 10/2022
DO - 10.1109/ACCESS.2022.3212918
LA - eng
N2 - <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>

PY - 2022
SE - 108134
T2 - IEEE Access
TI - Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset
UR - https://arxiv.org/abs/2210.05282
VL - 10
ER -