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TOMAŠOV, A. BUKOVSKÝ, J. ZÁVIŠKA, P. HORVÁTH, T. LÁTAL, M. MÜNSTER, P.
Originální název
Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The paper introduces an innovative object classification method for urban environments, employing distributed acoustic sensing (DAS) to address the complexities of urban landscapes. Utilizing omnipresent optical telecommunication cables, our approach involves a modified convolutional neural network (CNN) with transfer learning, achieving up to 85% accuracy. This method reuses most of the original network for feature extraction, with a final layer customized for new urban datasets – initially trained at the Brno University of Technology and then adapted to city center data. The model effectively identifies urban elements like vehicles and pedestrians, showcasing the potential of DAS for real-time classification in urban management and planning.
Klíčová slova
Convolutional neural networks;Machine learning;Cross validation;Fiber optics;Telecommunications;Fiber optics sensors
Autoři
TOMAŠOV, A.; BUKOVSKÝ, J.; ZÁVIŠKA, P.; HORVÁTH, T.; LÁTAL, M.; MÜNSTER, P.
Vydáno
18. 6. 2024
ISSN
0277-786X
Periodikum
Proceedings of SPIE
Ročník
13017
Číslo
1301715
Stát
Spojené státy americké
Strany počet
5
BibTex
@inproceedings{BUT188412, author="Adrián {Tomašov} and Jan {Bukovský} and Pavel {Záviška} and Tomáš {Horváth} and Michal {Látal} and Petr {Münster}", title="Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks", booktitle="Machine Learning in Photonics", year="2024", journal="Proceedings of SPIE", volume="13017", number="1301715", pages="5", doi="10.1117/12.3021990", issn="0277-786X" }