# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 6 Conclusions  
This study demonstrates that classifying UXO from magnetometer data, particularly when the data is generated from complex digital twins incorporating remanent magnetization, presents significant challenges that expose the limitations of classical machine learning models. We found that popular models like Random Forest can easily overfit, which we think could be the result of subtle artifacts within the simulated data. These artifacts--potentially arising at the boundaries of different mesh densities used to ensure feasible computation times in the digital twin--while visually indistinguishable, can be exploited by tree-based models. This overfitting leads to a drastic degradation in performance when the model is faced with even minor variations in the input data. Fortunately, the proposed Convolutional Neural Network (CNN) architecture proves to be highly resilient to these variations. Moreover, it significantly outperforms the classical models, achieving superior predictive accuracy. Our ablation studies confirmed that architectural components such as element-wise batch normalization and skip connections are critical to this stability and performance. Furthermore, our results show that a more compact \"two-block\" model offers a compelling trade-off, achieving comparable performance to a deeper \"three-block\" model while having 1.5 times fewer learnable parameters. This finding is particularly important for developing computationally efficient models suitable for on-site data processing during field studies. The inclusion of metadata was also shown to provide a consistent performance improvement. The resilience analysis yields critical insights for the design of practical field surveys. Our results indicate that signal drift and time-warping are the most detrimental distortions, causing the steepest decline in model performance. This observation has direct implications for AUV survey design, underscoring the danger of electromagnetic interference--for  
Figure 6: The impact of linear drift augmentation on model performance. (**a**) Examples of signals with different levels of added drift. (**b**) Balanced accuracy as a function of the drift amplitude. instance, from one vehicle's propulsion system causing signal drift on a nearby vehicle's sensor. It also highlights the importance of precise vehicle positioning and maintaining a constant survey speed, as variations in velocity are equivalent to time-warping distortions that can severely impact classification accuracy. In conclusion, this work not only presents a robust deep learning architecture for UXO classification, but also highlights the practical challenges and considerations for deploying such models. We believe these findings advance the understanding of neural network applications in this domain and provide actionable guidelines for improving both model design and data acquisition protocols. For future research, we anticipate that significant performance gains could be achieved by fusing magnetometry data with complementary sensor modalities, such as sonar imagery [12], to create a more comprehensive and reliable classification system. Conceptualization, P.S. and M.B. ; methodology, P.S. and M.B. ; software, P.S. ; validation, P.S. and M.B. ; formal analysis, M.B. ; investigation, P.S. and M.B. ; resources, P.S. and M.B. ; data curation, P.S. ; writing--original draft preparation, P.S. and M.B. ; writing--review and editing, M.B. ; visualization, P.S. ; supervision, M.B. ; project administration, M.B. ; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Silesian University of Technology proj. no BK-227/RM4/2025, and by the project 32/014/SDW/005-61 of Polish Ministry of Science and Higher Education, and by Polish National Centre for Research and Development (NCBR) under project number POIR.01.01.01-00-1023/21-00. Not applicable.