# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 2 Materials and Methods
### Convolutional Neural Networks: Knowledge Transfers and Hyperparameters  
This initial study demonstrates that, during night-time navigation, it is possible to improve the course-keeping of a merchant vessel, which is still commonly guided by leading lights. As a result, the human factor becomes critically important, with direct implications for fuel efficiency, operational delays, and navigational safety. To address this, we tested different neural network training strategies. The first approach was based on transfer learning, while the second involved adjusting hyperparameters such as the learning rate and the number of iterations needed to ensure convergence of the gradient descent algorithm used throughout the training process.  
The transfer learning approach makes use of a pre-trained network that is adapted to a specific image recognition task. In this case, we selected SqueezeNet, a convolutional neural network with 18 layers, which is available in a pre-trained version trained on over one million images from the ImageNet database. These reference images include vehicles and everyday objects, offering a solid foundation for transfer learning in related visual tasks. Therefore, the purpose of this approach is to leverage the feature recognition capabilities already learned by SqueezeNet and fine-tune it with domain-specific images, reducing training time while maintaining accuracy.  
For the training setup, the convolutional base of SqueezeNet (up to the final fire module) was frozen to retain general feature extraction, while the classifier layers were replaced and fine-tuned using our simulator-acquired dataset. The model was trained with  
Figure 1: View of the navigation bridge within the Transas NTPRO 5000 simulator (Transas Marine Ltd., Saint Petersburg, Russia) used for data acquisition.  
a batch size of 32 using the Adam optimizer (learning rate = 0.0005, \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999), and a stepwise learning rate decay was applied every 60 epochs. Although weight decay was not included in the baseline, L2 regularization (\(\lambda\) = 0.01) was tested to reduce overfitting. Dropout (\(p\) = 0.5) was applied to the fully connected layers, and data augmentation techniques, such as horizontal flips, brightness shifts, and random cropping, were used to increase variability in night-time lighting conditions.  
SqueezeNet was chosen over other lightweight CNNs (e.g., MobileNet, EfficientNet) due to its minimal parameter count (~1.25 M), fast inference, and low memory footprint, which are critical for real-time performance and potential integration into onboard navigation systems with constrained computational resources.  
Model Selection Justification  
Convolutional neural networks (CNNs) were selected for this study due to their well-established capacity to extract spatial features from image-based data, particularly in low-light environments where leading lights may be partially occluded or distorted by ambient illumination. Although alternative architectures, such as Fully Connected Networks (FCNs), Recurrent Neural Networks (RNNs), and Transformer-based models, were considered, CNNs provided the best trade-off between accuracy, inference speed, and real-time feasibility for onboard assistance systems. The following table (Table 1) summarizes the comparative strengths and weaknesses of each architecture:  
We selected SqueezeNet, a compact CNN with significantly fewer parameters than larger networks, such as ResNet or Inception, which allowed faster training and inference while preserving accuracy. Transfer learning with ImageNet-pre-trained weights also accelerated convergence and improved generalization.