# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 4 Results and Analysis
### Results of the Ablation Experiments  
This paper demonstrates the necessity and effectiveness of each module in the proposed model through ablation experiments. Under the same environmental configurations as mentioned in Section 3.2, the model was trained for 300 epochs on the custom dataset. The CBAM module and the DSC module were alternately added and removed to validate the effectiveness of the proposed modules. During the experiments, all other parameters were kept constant, except for the use of the specific modules.  
The quantitative analysis results of the ablation experiments are presented in Table 2, where Experiment 1 serves as the baseline network, U-Net, upon which all subsequent experiments are conducted. As evident from the data in the table, the integration of the  
Figure 7: Comparison of positive and negative pixels.  
CBAM module significantly enhances the model's performance. Specifically, compared to the original network, the network augmented with the attention mechanism experiences a slight decrease in \(P\) but achieves a notable 5.39% increase in \(R\), leading to a 3.98% improvement in the comprehensive evaluation metric, \(F1\)-score. Furthermore, by comparing Experiment 3 with Experiment 4, it is observed that, while \(P\) undergoes a marginal decline, \(R\) improves by 2.83%. However, due to the inherent trade-off between precision and recall, maintaining simultaneous growth for both metrics is challenging. This indicates that the incorporation of the CBAM module enhances the network's recall capability, manifesting in a more comprehensive detection of road cracks within images. Consequently, this approach addresses the issue of incomplete road crack detection, which is the primary objective of this study.  
Through the comparison between Experiment 1 and Experiment 3, it can be observed that the network with the addition of the DSC module exhibits a slight improvement in accuracy, accompanied by a substantial 5.49% increase in recall. This enhancement also translates to a 4.03% improvement in the \(F1\)-score. Additionally, by contrasting Experiment 2 with Experiment 4, it becomes evident that the incorporation of DSC enhances both \(R\) and \(F1\), albeit with a minor decrease in \(P\). However, this minor decrement in \(P\) is deemed tolerable in light of the overall improvement in model performance. Considering the cumulative effects, the inclusion of both modules optimizes the overall performance, and the removal of any of these modules from the model would inevitably impact the segmentation results.  
The qualitative results of the ablation experiments are depicted in Figure 8, where the white portions represent the network's recognition outcomes, the black portions correspond to the background, and the red boxes highlight specific regions. Discernible from the red boxes, the ADSC-U-Net model demonstrates a commendable performance in detecting fine cracks and discontinuous crack regions. In contrast, U-Net exhibits a lesser efficacy in detecting elongated and thin crack segments, resulting in discontinuities. The individual addition of the DSC module in Dynamic Snake Convolution U-Net (DSC-U-Net) and the CBAM mechanism in Attention U-Net (Att-U-Net) enhances the completeness of crack detection to a certain extent. Furthermore, the fusion of both the DSC and CBAM within the model elevates the capability for comprehensive crack detection even further, which is manifested in the elevated R value among the evaluation metrics.