# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 4 Conclusions  
This study leads to the following main conclusions, based on experimental and analytical findings: Artificial intelligence (AI) can reliably identify correct and incorrect navigation based on visual input, using image recognition techniques.  
AI is capable of distinguishing between leading lights and background sources, such as lighthouses or horizon lighting, allowing it to consistently identify correct leading alignment.  
This technology has the potential to reduce human factor-related accidents, improving operational safety during night-time navigation.  
1. AI offers clear advantages over human observation in classifying navigation scenarios based on visual input. Furthermore, expanding the number of classification cate  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Image** & **Accuracy (\%)** \\ \hline Doubt & 100\% \\ NOT OK–Low Bow Deviation & 19\% \\ NOT OK–Medium Starboard Deviation & 0\% \\ NOT OK–Medium Port Deviation & 41\% \\ OK 1 Light & 70\% \\ OK 2 Lights & 83\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision of the convolutional neural network for each different situation.  
gories beyond a simple \"OK/NOT OK\" binary system leads to more informative and accurate results.
* Custom training of the neural network based on the distance between the two leading lights can improve detection performance. Even using a medium-resolution camera, the system demonstrated greater reliability than human visual interpretation in identifying correct alignment at various distances.
* An initial human-based classification is still necessary to enable effective supervised training. However, machine learning techniques, such as clustering algorithms, could assist in automating this step and improving labeling consistency, thereby enhancing final model accuracy.
* Real-time testing confirmed the effectiveness of the visual AI system compared to human judgment, a result that should be seriously considered by shipyards and ship-owners. In the near future, this technology is expected to be implemented onboard as a standard aid to navigation.  
Conceptualization, M.V.N., G.C.F., B.S.F. and J.A.O.; Methodology. M.V.N., G.C.F., B.S.F. and J.A.O.; Software, M.V.N., G.C.F., B.S.F. and J.A.O.; Validation, M.V.N., G.C.F., B.S.F. and J.A.O.; Formal analysis, M.V.N., G.C.F., B.S.F. and J.A.O.; Investigation, M.V.N., G.C.F., B.S.F. and J.A.O.; Resources, M.V.N., G.C.F., B.S.F. and J.A.O.; Data curation, M.V.N., G.C.F., B.S.F. and J.A.O.; Writing--original draft, M.V.N., G.C.F., B.S.F. and J.A.O.; Writing--review & editing, M.V.N., G.C.F., B.S.F. and J.A.O.; Visualization, M.V.N., G.C.F., B.S.F. and J.A.O.; Supervision, M.V.N., G.C.F., B.S.F. and J.A.O.; Project administration, M.V.N., G.C.F., B.S.F. and J.A.O.; Funding acquisition, M.V.N., G.C.F., B.S.F. and J.A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors wish to express their gratitude to the University of A Coruna for its collaboration during the development of this research.  
The authors declare no conflict of interest.