# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Number of Iterations
#### 4.1.2 Impact of Iteration Count on SegNet  
This subsection examines the performance of the SegNet model in semantic segmentation of crack images across different iteration counts. The model was trained using five iteration settings: 5000, 10,000, 30,000, 45,000, and 75,000 iterations. A dataset of 300 images was used, generated by augmenting 50 original images comprising 17 images of general wall cracks, 17 images of weathered wall cracks, and 16 images of brick wall cracks. The dataset was split into 70% for training and 30% for validation. Performance metrics, including training and validation accuracy as well as training and validation loss, were recorded. Additionally, 15 original images comprising five images each of general wall cracks, weathered wall cracks, and brick wall cracks were used for application analysis, with model performance evaluated using the Intersection over Union (IoU) metric.  
Figure 10 presents the training and validation results of the SegNet model across different iteration counts, including accuracy and loss. Figure 11 displays the IoU results for each iteration count. At 5000 iterations, the training and validation accuracies were 99.01% and 98.47%, respectively, with training and validation losses of 0.331 and 0.338. These results indicate strong model performance; however, the relatively high loss values suggest room for further optimization. The IoU score of 0.825 reflects good segmentation accuracy, demonstrating the model's ability to effectively detect and segment cracks while still allowing for potential improvement.  
At 10,000 iterations, training and validation accuracies slightly adjusted to 98.72% and 98.53%, with corresponding losses of 0.214 and 0.225. The significant reduction in training and validation loss indicates enhanced convergence performance, while accuracy remains at a high level. The IoU score increased slightly to 0.832, suggesting an improvement in segmentation precision. At 30,000 iterations, training and validation accuracies further improved to 98.88% and 98.62%, with losses decreasing to 0.045 and 0.066. These results demonstrate stronger convergence and generalization. However, the IoU score declined slightly to 0.823, suggesting that despite improvements in accuracy, the model's segmentation performance in practical application showed a slight reduction.  
Figure 11: IoU results for different iteration counts using SegNet.  
Figure 10: Training and validation results of different iterations using SegNet for **(a)** accuracy and (b) loss coefficient.  
At 45,000 iterations, training and validation accuracies reached 99.13% and 98.62%, while losses decreased significantly to 0.019 and 0.052. These results indicate that the model achieved high precision and stability. The IoU score remained at 0.832, marking the best observed segmentation performance. At 75,000 iterations, the training accuracy approached 99.99%, while validation accuracy increased to 99.39%. However, validation loss increased slightly to 0.083, indicating the emergence of mild overfitting. The IoU score measured 0.826, suggesting that excessive iteration may have slightly degraded the model's generalization performance.  
Overall, across all iteration settings, training and validation accuracies consistently exceeded 98%, while training loss continued to decrease as iterations increased. However, validation loss was minimized at 45,000 iterations, and the best IoU evaluation results were also achieved at this point. These findings indicate that at 75,000 iterations, the model exhibited signs of overfitting. Therefore, the optimal iteration count for SegNet in this study is determined to be 45,000.  
This study found that GoogleNet and SegNet produced identical results. A higher number of iterations does not necessarily lead to better performance; excessive iterations may result in overfitting. These findings are consistent with the results of [54; 55].