# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 4 Experiment
### Ablative Experiment  
To more effectively validate the performance of the morphology-integrated irregular dynamic convolution (MIDConv) and the cross-dimensional interactive attention module (CDIAM) in corridor crack detection, an ablation experiment was devised to examine the effectiveness of these two improvements in extracting crack features, as show in Figure 11, the red box represents a relatively large difference. The comparison of evaluation metrics are shown in Table 2.  
Figure 10: Comparison of visualization results of different algorithms.  
Figure 11 indicates that by introducing irregular dynamic convolution to expand the fixed-shaped and sized convolution kernels in the original U-Net network architecture, the receptive field of the improved network is correspondingly enlarged. During crack feature extraction, U-Net-MIDConv acquires more abundant feature information, allowing the segmentation model to identify more crack features in the prediction map. It can be easily observed that while the crack segmentation results of U-Net-MIDConv are more effective than the original U-Net network and visually more comprehensive, U-Net-MIDConv still exhibits slight omissions in some details. In this case, by incorporating the cross-dimensional interactive attention module, pixel-level relationships between cracks are reconstructed using global channel information, minimizing information loss during high-dimensional feature extraction. Additionally, experimental results from adding the cross-dimensional interactive attention module to the original U-Net network alone validate its ability to handle details at a fine-grained level.  
After computing the four metrics of MIOU, MPA, F1-score, and accuracy for the test dataset, the results are presented in the table. The results indicate that for the original U-Net network, independently adopting the morphology-integrated irregular dynamic convolution method (MIDConv) or adding the cross-dimensional interactive attention module (CDIAM) both improve the model's detection performance, demonstrating that while MIDConv or CDIAM alone are effective in corridor crack feature extraction, they also have shortcomings. By combining the two, the model can both extract features according to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MIOU (\%)** & **MPA (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline U-Net & 79.1 & 87.15 & 84.36 & 99.1 \\ U-Net+CDIAM & 82.36 & 89.54 & 85.68 & 99.27 \\ U2 Net+MIDConv & 82.78 & 91.56 & 87.02 & 99.30 \\ Ours & 84.44 & 92.05 & 87.75 & 99.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments of different model structures.  
Figure 11: Comparison of crack detection results in ablation experiments.  
the morphological features of the cracks and also, remarkably, extract crack features across different dimensions.  
Additionally, to further validate the generalization ability of the algorithm proposed in this paper, the crack dataset used in the literature DeepCrack and the public crack dataset Crack500 were trained and tested using the method in this paper. The indicators of the test set are shown in Table 3. Through comparative analysis of metrics results, it is evident that the improved algorithm proposed herein demonstrates strong generalization capability under the same environmental configuration.