# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 2 Recent Advances in UXO Classification  
The field of Unexploded Ordnance (UXO) classification has rapidly evolved with the emergence of advanced sensor technologies and sophisticated data analysis methods. While magnetometers have long been a mainstay for detecting ferromagnetic UXO underwater, recent years have seen the increasing integration of electromagnetic induction (EMI) sensors, which are particularly effective for detecting both ferrous and non-ferrous metallic objects. EMI sensors induce secondary electromagnetic fields in conductive materials, enabling discrimination between UXO and benign clutter, even in challenging environments where magnetic signatures alone may be ambiguous [4, 5]. However, while EMI sensors provide valuable information by actively probing conductive targets and can detect both ferrous and non-ferrous metals, they generally exhibit lower signal-to-noise ratios, shorter effective detection ranges, higher power demands, and greater sensitivity to environmental factors than magnetometers, which remain more robust and efficient for passive detection of ferromagnetic UXO in underwater environments. Quantum magnetometry is an emerging technology for enhancing Unexploded Ordnance (UXO) detection in underwater environments. By exploiting quantum mechanical phenomena such as spin coherence and quantum superposition, quantum magnetometers--particularly those based on nitrogen vacancy (NV) centers in diamond--offer sensitivity and precision far surpassing traditional sensors, enabling the detection of even weak magnetic anomalies associated with UXO. Recent field trials and industrial initiatives, including the integration of NV magnetometers into autonomous underwater vehicles (AUVs), demonstrate the feasibility of compact, low-power, and robust quantum sensors for real-time mapping and localization tasks [6]. Despite challenges related to miniaturization, environmental robustness, and power management, advances in nanofabrication and quantum optics are paving the way for deploying these sensors in operational settings, promising a significant leap in the accuracy and efficiency of underwater UXO classification. In the context of autonomous data acquisition, [PERSON] et al. [7] describe the integration of multiple submersible fluxgate magnetometers with a hovering autonomous underwater vehicle (AUV) as part of the EU-funded BASTA project. Their system enables high-resolution magnetic surveys at low altitudes and velocities, improving the detection and localization of munitions on the seafloor. The AUV-mounted magnetometers, arranged in a vertical triangle, allow for the calculation of spatial magnetic gradients, which enhance the discrimination between UXO and nonUXO. Field trials conducted at munitions dump-sites in the Baltic Sea and controlled tests with surrogate objects demonstrate the system's capability to reliably detect munitions shells as small as 81 mm from a distance of 1 m above the seafloor. The authors also emphasize the importance of integrating high-resolution camera systems for ground-truth and final confirmation of UXO. One of recent attempts to automate underwater Unexploded Ordnance (UXO) detection involves the application of inverse modeling techniques to magnetic data analysis. [PERSON] et al. [8] proposed a framework that fits measured magnetic anomalies to physically based parametric models, specifically employing the magnetic dipole model to represent UXO signatures. This approach enables the extraction of intrinsic object parameters such as magnetic polarizabilities, orientation, and burial depth, which are essential for accurate classification. By grounding classification algorithms in these physically meaningful parameters, the method improves discrimination between UXO and nonUXO. Validation on complex marine datasets with multiple targets and varying burial conditions demonstrated enhanced detection confidence and a reduction in false positives.