# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Number of Images
#### 4.2.1 Impact of Image Quantity on GoogleNet  
To assess the performance of the GoogleNet model under varying image quantities, datasets of 34, 50, 66, 100, and 130 images were used, with 70% allocated for training and 30% for validation. The number of images in each category is detailed in Table 3. The model was trained and validated using the optimal iteration count (75) to evaluate the effect of image quantity on learning capability.  
The trained GoogleNet model was further tested on an independent set of 34 images, comprising six images of cracks on plain walls, six on Mottled walls, six on brick walls, and 16 without cracks. Model performance was assessed using comprehensive evaluation metrics. Figure 12 presents the training and validation results for different image quantities, while Figure 13 illustrates the corresponding evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & & & & \\
**Images for Training** & & & & \\
**and Validation** & & & & \\ \hline
34 & 6 & 6 & 5 & 17 \\
50 & 9 & 8 & 8 & 25 \\
66 & 11 & 11 & 11 & 33 \\
100 & 17 & 17 & 16 & 50 \\
130 & 22 & 22 & 21 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Using GoogleNet on different images and different types of images.  
sive evaluation metrics yielded an accuracy of 91.67%, precision of 84.62%, recall of 100%, and an F1-score of 0.917. While the model successfully detected all cracks, it exhibited a high false positive rate, highlighting the need for improved precision and stability. When trained and validated with 50 images, the training and validation accuracies were 90% and 86.67%, respectively, with losses of 0.058 and 0.083. While the training accuracy decreased slightly, the validation accuracy remained unchanged, and the validation loss showed minor improvement. However, the model's generalization ability was still insufficient. The evaluation metrics indicated an accuracy of 86.67%, precision of 78.95%, recall of 100%, and an F1-score of 0.882. The drop in precision and F1-score suggests that further optimization is needed, even with an increased number of training images.  
For the model trained and validated with 66 images, the training and validation accuracies were 100% and 100%, respectively, with losses of 0.0001 and 0.004. Notably, all evaluation metrics--accuracy, precision, recall, and F1-score--reached their highest values, indicating superior model performance under this condition. Using 100 images for training and validation, the model achieved training and validation accuracies of 100% and 90%, with losses of 0.01 and 0.135. Both training and validation accuracies improved, and validation loss significantly decreased, demonstrating enhanced generalization ability. The evaluation metrics showed an accuracy of 90%, precision of 83.33%, recall of 100%, and an F1-score of 0.909. Although precision and F1-score slightly declined, overall performance remained at a high level. When trained and validated with 130 images, the training and validation accuracies were 100% and 96.67%, respectively, with losses of 0.04 and 0.014. The evaluation metrics showed an accuracy of 96.67%, precision of 93.33%, recall of 100%, and an F1-score of 0.968.  
Among all tested configurations, the model trained and validated with 66 images demonstrated the best evaluation performance when analyzing the independent set of 34 images, achieving 100% accuracy, precision, recall, and F1-score. This suggests that training with 66 images yields the most stable and accurate results, ensuring excellent learning and generalization capabilities.