# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Comparative Experiment Results and Analysis
#### 3.3.2 Analysis of DeepGlobe Roads Dataset Results  
In this section, we delve into the experimental outcomes derived from deploying the PCCAU-Net model, along with several benchmark models, on the DeepGlobe Road Dataset. Similar to the approach adopted for the Massachusetts Roads Dataset, our evaluation incorporates both qualitative and quantitative analyses, illustrated in Figure 6 and Table 3, respectively.  
Figure 6 presents road extraction results from five images, each embodying unique a challenge in diverse geographical settings: rural roads interspersed with natural scenery (Image 1), urban arterials suffering from significant occlusions (Image 2), suburban roads (Image 3), scenes with spectrally similar nonroad objects (Image 4), and highly intricate and dense urban road networks (Image 5).  
Our analysis reveals that PCCAU-Net exhibits exceptional capabilities in road identification and extraction across these varying contexts, notably excelling in complex settings featuring occlusions and spectral similarities between roads and other elements. This superior performance can be attributed to the fusion attention mechanism embedded within the model's Dual-Input Cross Attention (DCA) module, which dynamically integrates information from diverse sources, thereby enhancing both the accuracy and robustness of the model for road extraction tasks across a spectrum of intricate and varied environments.  
The results presented in Table 3 unequivocally indicate that the PCCAU-Net model outperforms a range of benchmark models--including U-Net, ResU-Net, DeepLab V3+, and SDUNet--across key performance metrics on the DeepGlobe Road Dataset. Of particular salience is the marked enhancement PCCAU-Net achieves in terms of precision, recall, and Intersection-over-Union (IoU). Specifically, the model attains a precision of 87.424%, a recall of 88.293%, and an IoU as high as 80.113%, thereby eclipsing the performance of other models under comparable conditions. For instance, when juxtaposed with U-Net, PCCAU-Net surpasses the latter's IoU by nearly five percentage points, thereby revealing its exceptional robustness and accuracy in confronting a multitude of geographical challenges, such as road occlusion and intricate road networks. Although the DeepLab V3+ model also performs commendably in the realms of precision and recall, its IoU score falls short of surpassing that of PCCAU-Net, further accentuating the latter's superior performance in image segmentation tasks. This quantitative evidence underscores PCCAU-Net's prowess in tackling complex road extraction tasks, largely attributable to its meticulously designed model, particularly the synergistic integration of Pyramid Pathway Input and DCA modules. Such innovative design elements not only bolster the model's discriminative accuracy in intricate settings but also enhance its adaptability and resilience across varied geographical environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline U-Net & 83.436 & 84.454 & 75.365 \\ \hline Resunet & 86.098 & 86.085 & 77.819 \\ \hline DeeplabV3+ & 85.489 & 85.653 & 78.158 \\ \hline SDUNet & 86.061 & 85.209 & 77.238 \\ \hline PCCAU-Net & 87.424 & 88.293 & 80.113 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative Results Evaluation of Five Models on DeepGlobe Road Dataset.  
Figure 6: Extraction results of five models on the DeepGlobe Road Dataset.