# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 5 Experiments and Analysis of Results
### Comparison of the Proposed Model with Existing Models  
Comparative experiments were conducted on the CRACK500 dataset to evaluate the superiority of the improved U-Net model for pavement crack segmentation. RAO-UNet [26], Crack-UNet [30], and DeepLabV3+ [27] were used as comparison methods in this paper. Precision, recall, F1-score, and mIOU were used as evaluation indicators to illustrate the advancement of the proposed method. The experimental loss functions of the model and the comparison models are shown in Figure 14.  
In the loss function graph, the horizontal coordinate represents the number of iterations, and the vertical coordinate is the network loss value. Comparing the network loss rate of the comparison model, it can be seen that the network loss rate of the method in this paper was the lowest among these networks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIOU/\%** & **Precision/\%** & **F1-Score/\%** & **Recall/\%** \\ \hline The new model & 81.62 & 86.28 & 89.30 & 92.54 \\ The original model & 79.33 & 82.93 & 85.84 & 88.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the comparison of the two models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIOU/\%** & **Precision/\%** & **F1-Score/\%** & **Recall/\%** \\ \hline RU-Net & 75.88 & 80.39 & 83.90 & 86.73 \\ RNU-Net & 76.56 & 82.21 & 84.28 & 88.38 \\ RPU-Net & 79.89 & 84.01 & 86.73 & 89.63 \\ RGU-Net & 80.13 & 85.33 & 88.76 & 90.11 \\ RNGU-Net & 82.90 & 88.10 & 90.25 & 94.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ablation experiments. The experimental accuracies of the model in this paper and the comparison models are shown in Figure 15.  
The accuracy of this experiment was recorded once for every two iterations: the horizontal coordinate represents the number of iterations, and the vertical coordinate represents the accuracy of each model. From the diagram, it can be seen that the accuracy of the model in this paper was the highest, reaching 96.01%, followed by the RAO-UNet model at 94.46%, the DeepLabV3+ model at 93.16%, and the Crack-UNet model, with an accuracy of 90.91%. The results of different models in highway pavement crack segmentation are shown in Figures 16-18. The segmentation results of the above methods are summarized in Table 6.  
From the above table, it can be seen that the method in this paper was significantly better than the other segmentation method models in the experiment. In terms of accuracy performance, the mean intersection over union of the model proposed in this paper reached 83.8%, and the accuracy rate reached 89.60%, which was 2.78% higher than the Crack-UNet model and 9.59% higher than the DeepLabV3+ model. The model proposed in this paper achieved 95.83% in the F1-score, and performed especially well in the Recall rate, which was better than the other methods. It was 12.87% higher than the Crack-UNet model, 4.29% higher than the DeepLabV3+ model, and 5.59% higher than the RAO-UNet model. In order to verify the performance of the network more intuitively, the segmentation results of these four methods are shown in Figure 19. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIOU/\%** & **Precision/\%** & **F1-Score/\%** & **Recall/\%** \\ \hline Crack-UNet & 76.04 & 86.82 & 84.85 & 82.96 \\ DeepLabV3+ & 77.34 & 80.01 & 85.39 & 91.54 \\ RAO-UNet & 82.56 & 89.15 & 89.69 & 90.24 \\ Ours & 83.80 & 89.60 & 92.61 & 95.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of pavement crack segmentation results. Figure 17: Precision indicator diagrams for the four models. Figure 18: Recall index diagram of the four models. From the above image, it can be seen that among the segmentation effects of these methods, the Crack-UNet network had the worst segmentation effect, with a large area of missed detection, and the segmentation effect in the detail part was not very good, which is very different from the real label value.