Detail publikace
Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity
TOMAŠOV, A. ZÁVIŠKA, P. DEJDAR, P. KLÍČNÍK, O. DA ROS, F. HORVÁTH, T. MÜNSTER, P.
Originální název
Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
This paper presents an advanced perimeter protection system that integrates phase-sensitive Optical Time-Domain Reflectometry ( Φ -OTDR) with Convolutional Neural Networks (CNNs) for real-time event classification near optical fibers. The proposed approach enhances traditional security methods by providing robust monitoring in challenging environments, such as low visibility and large-scale areas. We evaluated multiple signal preprocessing techniques, including Fast Fourier Transform (FFT), Redundant Discrete Fourier Transform (RDFT), Discrete Wavelet Transform (DWT), and Mel-Frequency Cepstral Coefficients (MFCC), to optimize classification accuracy and computational efficiency. While MFCC achieved the highest accuracy (85.61%), RDFT provided the best balance between performance (85.47%) and real-time feasibility, making it the preferred method for deployment. The system successfully differentiates events such as vehicle movement, fence manipulation, and construction work, while anomaly detection capabilities further enhance security by identifying irregular activities with minimal error. These findings demonstrate the potential of integrating fiber-optic sensing with deep learning to develop scalable, real-time perimeter protection solutions for critical infrastructure, border surveillance, and urban security.
Klíčová slova
Convolutional neural networks;distributed acoustic sensing;event classification;perimeter protection;phase-sensitive optical time-domain reflectometry
Autoři
TOMAŠOV, A.; ZÁVIŠKA, P.; DEJDAR, P.; KLÍČNÍK, O.; DA ROS, F.; HORVÁTH, T.; MÜNSTER, P.
Vydáno
8. 4. 2025
Nakladatel
IEEE
Místo
Online
ISSN
2169-3536
Periodikum
IEEE Access
Ročník
13
Číslo
1
Stát
Spojené státy americké
Strany od
63600
Strany do
63610
Strany počet
11
URL
Plný text v Digitální knihovně
BibTex
@article{BUT197717,
author="Adrián {Tomašov} and Pavel {Záviška} and Petr {Dejdar} and Ondřej {Klíčník} and Francesco {Da Ros} and Tomáš {Horváth} and Petr {Münster}",
title="Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity",
journal="IEEE Access",
year="2025",
volume="13",
number="1",
pages="63600--63610",
doi="10.1109/ACCESS.2025.3558594",
issn="2169-3536",
url="https://ieeexplore.ieee.org/document/10955273"
}