## 4 Results and Discussion  
### Evaluation of Model Training  
The evaluation metrics for the training results of the three variants of the digit detection model (YOLOv8) are presented in Table 5. Based on these results, the training configuration with a batch size of 8 was found to be optimal for each variant. This is evidenced by the highest accuracy, precision, recall, and F1-score values achieved with this setting. The YOLOv8 model employs a deep learning technique that does not require manual feature extraction; it can automatically extract relevant features from data and improve accuracy over time with training. This capability allows the model to be more adaptable in managing data variability, such as blur, perspective, rotation, and lighting factors. The YOLOv8 model was trained with a sufficiently large dataset and showed good generalization capability, enabling it to recognize objects, specifically seven-segment digits in this context, in unseen situations during training [25].  
Among the three trained YOLOv8 variants, the medium variant achieved the highest accuracy, recall, and F1-score, even though there were no significant differences in their metrics. Significant differences were observed only in the model size and inference time. The size and inference time of the YOLOv8-small variant were approximately half that of the medium variant and one-fourth that of the large variant. This is due to the simpler network structure of the YOLOv8-small variant compared to the YOLOv8-medium and YOLOv8-large variants.  
Figure 6 shows the training curves of the YOLOv8-small variant. The training and validation losses (box loss, classification loss, and distribution focal loss) steadily decreased throughout the training epochs, indicating proper convergence. The evaluation metrics--precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95--consistently improved and stabilized  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{3}{c}{**YOLOv8**} & \multicolumn{3}{c}{**YOLOv8m**} & \multicolumn{3}{c}{**YOLOv8l**} \\ \cline{2-10}  & **8 Batch** & **16 Batch** & **32 Batch** & **8 Batch** & **16 Batch** & **32 Batch** & **8 Batch** & **16 Batch** & **32 Batch** \\ \hline Accuracy (\%) & 99.70 & 99.64 & 99.62 & **99.73** & 99.66  
near the end of training. This shows that both variants were able to learn effectively and maintain high detection quality.  
Further insight into the performance of the YOLOv8-small variant is provided in Figure 7, which compares the confusion matrices of models trained with and without data augmentation. The matrix for the augmented model (right) exhibits stronger diagonal dominance and fewer misclassifications overall, indicating better digit detection and classification.  
Notably, the augmentation strategy (including rotation, shearing, and brightness adjustments) led to substantial improvements across most digit classes, particularly those that were previously underrepresented or prone to confusion. For instance, the number of correct predictions for digit '1' increased from 274 to 352, those for digit '3' improved from 64 to 74, and those for digit '10', which appeared far more frequently due to its representation of grouped digits, increased from 618 to 639. This confirms that data augmentation not only benefits overall performance but also helps mitigate the effects of class imbalance, improving the recognition of both frequent and less frequent digits. Although Class '10' was overrepresented, the model maintained high precision and recall across all classes, with minimal false positives and false negatives. These observations suggest that the augmented model generalizes well, even under skewed class distributions.  
Figure 6: Training results of the YOLOv8-small model.  
Figure 7: Confusion matrix of the YOLOv8-small model.