# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Image Categories
#### 4.3.1 Impact of Image Categories on GoogleNet  
This study evaluates three different types of crack images: cracks on plain walls, cracks on mottled walls, and cracks on brick walls. Four separate GoogleNet models were trained using different datasets. (1) General crack detection model trained on 66 images, including 11 images for each crack type and 33 non-crack images. (2) Plain wall crack model trained on 66 images, consisting of 33 crack images (expanded from 16 original images using data augmentation) and 33 non-crack images. (3) Mottled wall crack model trained on 66 images, consisting of 33 crack images (expanded from 16 original images) and 33 non-crack images. (4) Brick wall crack model trained on 66 images, consisting of 33 crack images (expanded from 15 original images) and 33 non-crack images.  
Each dataset was split into 70% for training and 30% for validation, with 75 training iterations. After training and validation, an additional 34 images (six plain wall crack images, six mottled wall crack images, six brick wall crack images, and 16 non-crack images) were used for application analysis, and the results were assessed using comprehensive evaluation metrics. Figures 16 and 17 present the training, validation, and comprehensive evaluation results for GoogleNet across different image categories. For the general crack detection model, both training and validation accuracies reached 100%, with training and validation losses of 7.2242 \(\times\) 10\({}^{-6}\) and 0.0038, respectively. The comprehensive evaluation metrics showed an accuracy of 100%, precision of 100%, recall of 100%, and an F1-score of 1.0, indicating perfect performance.  
For the plain wall crack model, training and validation accuracies were 100% and 84.85%, with training and validation losses of 0.002 and 0.626. The evaluation metrics showed an accuracy of 75.85%, precision of 70.75%, recall of 90%, and an F1-score of 0.79. While the model achieved perfect accuracy in training, its validation accuracy was lower, and the high validation loss suggested overfitting. The relatively low F1-score indicated room for improvement in handling this image type.  
For the mottled wall crack model, training and validation accuracies were 100% and 91.3%, with training and validation losses of 6.2705 \(\times\) 10\({}^{-6}\) and 0.257. The evaluation metrics showed an accuracy of 86.65%, precision of 80.95%, recall of 100%, and an F1-score of 0.895. This model demonstrated high accuracy and lower validation loss, suggesting better generalization. The F1-score, approaching 0.9, indicated strong performance in this category.  
For the brick wall crack model, both training and validation accuracies were 100%, with training and validation losses of 8.2255 \(\times\) 10\({}^{-6}\) and 0.0063. The evaluation metrics showed 100% accuracy, precision, recall, and an F1-score of 1.0. This model performed  
Figure 16: Training and validation results of different image categories using GoogleNet for (**a**) accuracy and (**b**) loss coefficient.  
Figure 17: Comprehensive evaluation metrics for different image categories using GoogleNet for (**a**) accuracy, (**b**) precision, (**c**) recall, and (**d**) F1-score.  
exceptionally well, achieving perfect results with minimal loss, indicating outstanding capability in detecting cracks on brick walls.  
An analysis of different image categories revealed a significant impact on GoogleNet's performance. The best results were obtained when the dataset included all image categories. The brick wall crack model performed second-best, achieving the same perfect evaluation results as the general crack detection model in application analysis. In contrast, the plain wall and mottled wall crack models exhibited weaker generalization, as indicated by lower validation accuracy and higher validation loss, despite achieving high training accuracy. Their evaluation metrics, including accuracy, precision, recall, and F1-score, did not reach optimal levels.