# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 3 Results and Discussion
### Accuracy Analysis in Visual Detection
Another source of misclassification involved confusion between NOT OK-Medium Bow and OK (one or two lights) categories. This likely stems from the inherent visual similarity between these cases, which also points to potential inconsistencies in the original human-based labeling. For instance, images labeled as containing two lights may, in fact, show only one, which may explain the moderate 70% accuracy (48 correct identifications out of 69) in this specific case. From this Table 2, it can be concluded that the model demonstrates a very high recognition rate for correct leading conditions, whether represented by one or two visible lights, as well as for doubtful situations. However, moderate precision was observed when identifying incorrect leading in the \"Low Bow Deviation\" category, likely due to the visual similarity to a correctly aligned configuration where both lights appear near the bow but are slightly misaligned vertically. Figure 21: Accuracy of the CNN (Weight Learning factor 0.2 and six classes and 180 epochs, Accuracy = 0.68). Figure 20: Confusion matrix (Weight Learning factor 0.2 and six classes 180 epochs). Importantly, the reduced accuracy in detecting incorrect leading is primarily due to confusion between different incorrect categories, rather than misclassification as correct leading, which the model consistently recognized with high accuracy. Therefore, this tool shows strong potential as a decision-support system to assist bridge officers, though it is not yet suitable for fully autonomous navigation. Finally, the main source of error was linked to misclassification between \"Low Bow Deviation\" and correct leading, a limitation that appears to stem from inconsistencies in the initial human labeling process prior to training. Consequently, future research will focus on increasing classification granularity and improving labeling accuracy to further enhance model performance. Nevertheless, this study presents certain limitations. First, the dataset was entirely generated under simulated conditions, which--although realistic--may not capture the full variability of real-world maritime environments. Additionally, the model showed reduced accuracy in detecting subtle misalignments, particularly in \"Low Bow Deviation\" scenarios, highlighting the challenge of fine-grained classification. Finally, a preliminary analysis of statistical robustness was performed based on classification accuracy, confusion matrices, and misclassification trends across different navigation scenarios. While the model shows high precision in identifying correct alignment, its performance in low-deviation conditions reveals areas for improvement. These observations underscore the need for more extensive statistical validation in future work, ideally supported by larger and more diverse datasets. Future work will focus on enhancing the labeling process, integrating real-world data from onboard cameras, and testing the model in operational conditions. The use of ensemble models or attention-based architectures may also be explored to improve detection in visually ambiguous or dynamic lighting environments. Finally, a direct baseline comparison--such as with human observer performance or conventional rule-based methods--has not been included in this study, as no formal benchmarking data under identical simulator conditions is currently available. Future work will address this limitation by collecting empirical baseline data to enable a more comprehensive performance evaluation of the proposed CNN-based model.