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Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS) Cover

Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

Polish Maritime Research
Volume 31 (2024): Issue 1 (March 2024)
By: Norbert Sigiel,  Marcin Chodnicki,  Paweł Socik and  Rafał Kot  
Open Access
|Mar 2024

Authors

Norbert Sigiel

n.sigiel@amw.gdynia.pl

Polish Naval Academy, Faculty of Navigation and Naval Weapons, Poland

Marcin Chodnicki

Air Force Institute of Technology, Aircraft Composite Structures Division, Poland

Paweł Socik

Polish Naval Academy, Faculty of Mechanical and Electrical Engineering, Poland

Rafał Kot

Polish Naval Academy, Faculty of Mechanical and Electrical Engineering, Poland
DOI: https://doi.org/10.2478/pomr-2024-0008 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
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Language: English
Page range: 77 - 84
Published on: Mar 29, 2024
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Deep Learning Neural Network (DLNN),
mine detection and classification,
sonar imagery,
Mine Countermeasure (MCM),
Automatic Target Recognition (ATR
Related subjects:
Geosciences,
Atmospheric science and climatology,
Engineering,
Introductions and overviews,
Engineering, other,
Life sciences,
Life sciences, other

© 2024 Norbert Sigiel, Marcin Chodnicki, Paweł Socik, Rafał Kot, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

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