Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F25%3APU156283" target="_blank" >RIV/00216305:26220/25:PU156283 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/26:0197717
Result on the web
<a href="https://ieeexplore.ieee.org/document/10955273" target="_blank" >https://ieeexplore.ieee.org/document/10955273</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3558594" target="_blank" >10.1109/ACCESS.2025.3558594</a>
Alternative languages
Result language
angličtina
Original language name
Advancing Perimeter Security: Integrating DAS and CNN for Object Classification in Fiber Vicinity
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/VK01030121" target="_blank" >VK01030121: Middle range distributed fiber-optic sensing system for acoustic vibration and temperature monitoring on critical infrastructures</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
—
Volume of the periodical
2025
Issue of the periodical within the volume
13
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
63600-63610
UT code for WoS article
001469012900048
EID of the result in the Scopus database
2-s2.0-105003174027