## 4 Results and Discussion
### Depth Estimation
* Feature extraction: Image rotation encourages the model to learn invariant features. It requires the model to emphasize features consistent across orientations, thus aiding in the extraction of pertinent and informative features for accurate depth estimation. * Generalization: Exposure to an extensive array of angles equips the model with the ability to generalize its insights to novel orientations during inference. In essence, the choice of 160 different angles probably stems from a balance between creating a suitably diverse dataset and managing the computational demands of training. This numerical selection may have emerged through iterative experimentation and validation, ensuring that the model benefits from enhanced diversity while maintaining a manageable training process. Using the augmented data set, the same models (ResNet50, VGG16, VGG19, and DenseNet169) underwent rigorous training and evaluation. This assessment used a multifaceted set of evaluation metrics, encompassing accuracy, precision, recall, and F1 score, as shown in Table 5. These metrics serve as vital indicators of the efficacy of the model in distinct facets of the depth estimation task. These models were subjected to rigorous training spanning 100 epochs, with a batch size of 20 and a learning rate set at 0.001. This comprehensive evaluation regimen ensured meticulous scrutiny of the models' competence from various vantage points. From Table 5, it can be observed that:  
* All models attained substantial scores across evaluation metrics, indicating proficient performance in depth estimation. * DenseNet169 secured the highest values in all metrics, followed by VGG16, VGG19, and ResNet50. * The models demonstrated consistent alignment between accuracy, precision, recall, and F1 score, reflecting balanced performance in positive and negative classes. * In particular, the application of angle rotation as an enhancement technique yielded notable improvements in the evaluation metrics compared to the previous experiment with the original dataset. The progression of the training and testing process over 100 epochs is visually captured in the collection of four graphs shown in Figure 15. This visualization offers valuable insights: ResNet50 illustrates a gradual and consistent increase in accuracy across epochs, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet50 & 0.9212 & 0.9221 & 0.9212 & 0.9216 \\ VGG16 & 0.9324 & 0.9338 & 0.9324 & 0.9331 \\ VGG19 & 0.9294 & 0.9300 & 0.9294 & 0.9297 \\ DenseNet169 & 0.9523 & 0.9535 & 0.9523 & 0.9529 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics of different models after 100 epochs on the augmented dataset. albeit with a modest final value. On the contrary, VGG16 and VGG19 exhibit swift accuracy improvements in the initial epochs, followed by a more gradual enhancement rate. In particular, DenseNet169 demonstrates a consistent and rapid accuracy advancement throughout the epochs, culminating in a substantial final accuracy value. It is important to note that all models exhibit diminished accuracy fluctuations after each epoch compared to the earlier experiment, indicating an improved level of learning stability. Figure 15: Accuracy curves of (**A**) ResNet50, (**B**) VGG16, (**C**) VGG19, and (**D**) DenseNet169 models. In terms of training accuracy, a rapid increase was observed from epochs 1 to 10, rising from 0.4412 to 0.9055. Subsequently, the training accuracy continued to improve, but the rate of increase decreased with each epoch. Over the next ten epochs, the training accuracy increased by only 0.06, reaching 0.9645 by the 20 th training session. The use of the expanded training dataset of 70,400 images contributed to the improved accuracy of the DenseNet169 model. The model achieved an accuracy of more than 65%, indicating the importance of this research and the generated dataset to estimate the depth of absorption structures in near-infrared images.