Publication detail
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.
Original Title
Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity
Type
journal article in Web of Science
Language
English
Original Abstract
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.
Keywords
Convolutional neural networks;distributed acoustic sensing;event classification;perimeter protection;phase-sensitive optical time-domain reflectometry
Authors
TOMAŠOV, A.; ZÁVIŠKA, P.; DEJDAR, P.; KLÍČNÍK, O.; DA ROS, F.; HORVÁTH, T.; MÜNSTER, P.
Released
8. 4. 2025
Publisher
IEEE
Location
Online
ISBN
2169-3536
Periodical
IEEE Access
Year of study
13
Number
1
State
United States of America
Pages from
63600
Pages to
63610
Pages count
11
URL
Full text in the Digital Library
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"
}