ID:
publications-5107
Type:
Conference paper
Year:
2023
Authors:
Blachnik M.; Ε_x009a_ciegienka P.; DaΜ§browski D.
Title:
Preliminary Study onÎ’ Unexploded Ordnance Classification inÎ’ Underwater Environment Based onÎ’ theÎ’ Raw Magnetometry Data
Venue/Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI:
10.1007/978-3-031-48232-8_40
Research type:
Water System:
Technical Focus:
Abstract:
Unexploded ordnance (UXO) dumped in water reservoirs pose a serious environmental and human safety hazard. Various ways of economically solving this problem are being sought. One of them is the use of machine learning methods for the automatic classification of dangerous objects based on the recorded signals. The paper presents the preliminary results on the use of machine learning methods applied to raw magnetometry data generated in a virtual environment based on the concept of a digital twin. This introduces a different approach to a standard approach, which is based on the inverse problem, where the signals are mapped to the magnetic dipole model. Conducted research points out that the highest performance can be obtained with neural networks, and a direct classification based on the raw signals allows to achieve accuracy of up to 93% when no remanent magnetization is present. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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