# Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
## 4 Results and Discussion
### Comparison Results between Predicted and Labeled Images  
Figure 12 shows the crack prediction results of the metamodel trained using the results of the four segmentation architectures. This includes the prominent crack locations in the results of DeepLabv3+, which showed the best performance. It provides segmentation results that are more detailed according to the crack width, such as UNet, and it does not include noise from DeepLabV3 or DANet. In other words, detailed segmentation results for cracks are obtained while hardly including noise that can be mistaken for cracks, such as the discoloration or shadows of concrete. Even if overfitting occurs in the model used in the stacking ensemble or if the learning is not fully progressed, the results are improved after performing stacking ensemble learning. Figure 13 compares the highest IoU scores calculated by each split model for the test and validation sets. In the case of the existing model, the IoU score for the validation set was calculated at approximately 0.6 under the same learning and dataset conditions, and the IoU score for the test set ranged from approximately 0.4 to 0.6. This difference in IoU is due to that in upsampling stage of the network. FCN and DeepLabV3 showed a low IoU score, which is because the final segmentation image was derived through a one-step upsampling process with a high magnification of the feature extractor result. During one-step upsampling at high magnification, significant microcrack  
Figure 12: Images predicted by metamodel trained by the stacking ensemble technique. information may be lost, or tiny crack information may be overestimated. On the contrary, DANet or DeepLabV3+, which performed about two steps of the upsampling process, showed a high IoU of 0.6 among the four models and little overfitting appropriate for crack detection. UNet undergoes a four-step upsampling process, conducting an IoU of 0.6 for the validation set but a slightly lower IoU for the test set, indicating overfitting. In other words, the parameters required for learning increased due to many upsampling processes. It is considered that it is difficult to train UNet with the dataset used in this study sufficiently. In contrast, the IoU score for the metamodel test set, which reanalyzed all models' results, was 0.74, showing a high improvement. Table 1 shows the scores of the models and metamodel used for stacking ensemble learning in this study. A total of 1085 test data were used to calculate the scores of the models. Among the test data, 795 images had cracks, and the remaining 290 images had no cracks. When using a single model, the model with the highest score was DeepLabV3+ with an F1-score of 0.899, and DANet performed next with an F1-score of 0.844. UNet and DeepLabV3 showed the third and fourth subordinate performance, respectively. In particular, in the case of DeepLabV3, the scorer of all models showed a high score, confirming that it is a network that should be considered a priority when using a single model. Finally, in the case of the metamodel created by introducing the stacking ensemble learning technique, it was quantitatively confirmed that the F-1 score showed superior performance over 0.95.