Digital Twin for Training Set Generation for Unexploded Ordnance Classification
Ładowanie...
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
The use of machine learning methods for unexploded ordnance
(UXO) detection and classification is very limited. This limitation derives
from the lack of representative and enough large training data. To overcome
this issue we propose a construction of a digital twin where UXO and
non-UXO objects are represented using mathematical models in a simulated
Earth magnetic field. The use of digital twins allows for simulating and collecting
a large training set which can be used for training machine learning
models. In the conducted research we discuss obtained results and point out
several of the detected problems.
Opis
Słowa kluczowe
machine learning, artificial intelligence, UXO, unexploded ordnance, uczenie maszynowe, sztuczna inteligencja, UXO, niewybuch
Cytowanie
Ściegienka P., Blachnik M., Digital Twin for Training Set Generation for Unexploded Ordnance Classification. W: Progress in Polish Artificial Intelligence Research 4,
Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 163-164, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.24.