# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Number of Iterations
#### 4.1.1 Impact of Iteration Count on GoogleNet  
This subsection investigates the performance of the GoogleNet model across different iteration counts. A dataset of 66 images was used, consisting of 11 images of general wall cracks, 11 images of weathered wall cracks, 11 images of brick wall cracks, and 33 images without cracks. The model was trained and validated over 25, 50, 75, 100, and 125 iterations, with 70% of the images used for training and 30% for validation. To further evaluate the model's performance, an additional set of 34 images (including six images each of general wall cracks, weathered wall cracks, and brick wall cracks, along with 16 images without cracks) was used for application analysis. The results were assessed using comprehensive evaluation metrics. Figure 7: Crack analysis results using SegNet on images with different resolutions. Figure 8 presents the training and validation results of the GoogleNet model across different iteration counts, including accuracy and loss metrics. Figure 9 illustrates the comprehensive evaluation metrics, including accuracy, precision, and F1-score. Figure 8: Training and validation results of different iterations using GoogleNet for (**a**) accuracy and (**b**) loss coefficient. Figure 9: Comprehensive evaluation metrics for different iterations using GoogleNet for (**a**) accuracy, (**b**) precision, and (**c**) F1-score. At 25 iterations, the training and validation accuracies were 90% and 70%, respectively, with training and validation losses of 0.573 and 0.963. The comprehensive evaluation metrics yielded an accuracy of 70%, precision of 62.50%, recall of 100%, and an F1-score of 0.769. While the model performed well on the training data, its performance on validation data was relatively poor, indicating that it learned the training data effectively but struggled with unseen data. Although the model successfully detected cracks, it exhibited a high false positive rate. At 50 iterations, the training and validation accuracies improved to 90% and 97.83%, respectively, with training and validation losses decreasing to 0.088 and 0.024. The evaluation metrics showed an accuracy of 97.83%, precision of 95.65%, recall of 100%, and an F1-score of 0.978. As the iteration count increased, the model's validation accuracy improved significantly, approaching 100%. The reduction in training and validation loss further demonstrated enhanced generalization ability, allowing the model to better handle unseen data while significantly reducing false positives. At 75 iterations, the model achieved 100% accuracy on both the training and validation datasets, with near-zero loss, indicating exceptionally high precision and stability. All evaluation metrics, including accuracy, precision, recall, and F1-score, reached 100%, suggesting that the model had attained its optimal state. At this iteration count, the model perfectly detected cracks without false positives, achieving the best overall performance. At 100 iterations, the training accuracy remained at 100%, while validation accuracy decreased to 93.48%, with training and validation losses recorded at 0.0003 and 0.199, respectively. The evaluation metrics showed an accuracy of 95.65%, precision of 92.31%, recall of 100%, and an F1-score of 0.96. Although the model maintained perfect performance on the training data, its validation performance declined, suggesting the onset of overfitting. At 125 iterations, the training accuracy remained at 100%, but the validation accuracy further declined to 90%. Training and validation losses were recorded at 0.0002 and 0.46, respectively. The evaluation metrics showed an accuracy of 90%, precision of 83.33%, recall of 100%, and an F1-score of 0.909. The increasing validation loss and declining validation accuracy indicated a more pronounced overfitting effect, where the model over-adapted to the training data at the expense of generalization. Consequently, the model's precision in crack detection decreased, leading to a higher false positive rate.