Detail publikace

Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks

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"
}