# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 4 Results and Analysis
### Algorithmic Analysis and Comparison  
The optimized network model, ADSC-U-Net, is compared to existing mainstream models such as U-Net, SegNet, DeepLab, and the DeepCrack network. According to the experimental results presented in Table 3, the optimized network model exhibits slightly lower \(P\) than U-Net but surpasses other algorithms in terms of the average precision, accuracy, and recall. Considered comprehensively, the algorithm proposed in this paper can enhance the integrity and accuracy of road crack detection, thereby contributing to improving road traffic safety levels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **CBAM** & **DSC** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Experiment 1 & - & - & 75.66 & 60.45 & 64.21 \\ Experiment 2 & \(\surd\) & - & 75.32 & 65.84 & 68.19 \\ Experiment 3 & - & \(\surd\) & 75.78 & 65.94 & 68.24 \\ Experiment 4 & \(\surd\) & \(\surd\) & 74.44 & 68.77 & 69.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments of ADSC-U-Net.  
As shown in Table 3, the data indicates that the improved network exhibits significant enhancements in the comprehensive metrics of \(P\), \(R\), and \(F1\)-score. Compared to U-Net, the algorithm proposed in this paper slightly underperforms in \(P\) but demonstrates an 8.32% increase in \(R\) and a 5.21% improvement in \(F1\)-score. Considering these factors comprehensively, the detection results of ADSC-U-Net are superior to those of U-Net. When compared to SegNet, DeepLab, and DeepCrack, ADSC-U-Net shows improvements of 1.90%, 2.49%, and 11.64% in \(P\), respectively, and enhancements of 8.01%, 4.70%, and 59.58% in \(R\), respectively. Additionally, it exhibits improvements of 5.73%, 4.02%, and 55.87% in the comprehensive evaluation metric \(F1\)-score, respectively. DeepCrack performs poorly on the self-built dataset used in this paper, tending to predict targets as background.  
Using the dataset divided in Section 3.2 for training, a qualitative evaluation of the proposed algorithm, as well as SegNet, DeepLab, and DeepCrack, is conducted on the validation set. The comparison results between the proposed algorithm and the other networks, including U-Net, SegNet, DeepCrack, and DeepLab, are shown in Figure 9. Specifically, the red boxes highlight the target pixel regions detected by our method that were not detected by the other networks. As can be seen from Figure 9, the proposed method demonstrates superior performance in detecting discontinuous cracks, while U-Net, SegNet, and DeepLab exhibit relatively poorer performance, and DeepCrack tends to predict targets as background in our self-built dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline U-Net & 75.66 & 60.45 & 64.21 \\ \hline ADSC-U-Net & 74.44 & 68.77 & 69.42 \\ \hline SegNet & 72.54 & 60.76 & 63.69 \\ \hline DeepLab & 71.95 & 64.07 & 65.40 \\ \hline DeepCrack & 62.80 & 9.19 & 13.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of ADSC-U-Net and other algorithms.  
Figure 8: Results of the ablation experiments.