# Method for Constructing a Facade Dataset through Deep Learning-Based Automatic Image Labeling
## 5 Results
### Test Data Output and Analysis  
The test was conducted on the facade image (raw data) extracted from the road-view images and the results were analyzed. The test data were intended to determine if the pixel accuracy, mIoU, and facade information could be maintained and how it could be improved through distortion correction, even for characteristics that differed from the training data. Thirty facade images (referred to as original data) extracted from the original road views and 30 facade images (referred to as distortion correction data) extracted from the distortion-corrected road views were used as test data. As shown in Tables 11 and 12, when the original data and distortion correction data were compared, the average mIoU and pixel accuracy values of the distortion correction data were higher by an average of 0.05 and 0.09, respectively. In addition, as shown in Table 13, the accuracy of the facade information was also 0.07 higher than that of the original. The results show that the distorted and corrected data can be effectively labeled using the CMP database compared with the original data. In this study, we compared the CMP database and the distortion correction data by viewing the distortion correction data showing higher accuracy of the facade information as a representative image of the facade image extracted from road views. As shown in Tables 14 and 15, the average mIoU and pixel accuracy values of the distortion correction data were lower than the CMP database by 0.09 and 0.02, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Facade Information** & **Original Data** & **Distortion Correction Data** \\ \hline Dominant Color & 0.80 & 0.83 \\ Secondary Color & 0.76 & 0.80 \\ The Number of Floors & 0.83 & 0.93 \\ The Number of Buildings & 0.80 & 0.90 \\ Average & 80 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Facade information accuracy of original and distortion correction data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Original Data** & **Distortion Correction Data** \\ \hline Walls & 0.52 & 0.69 \\ Windows & 0.27 & 0.53 \\ Entrances & 0.338 & 0.50 \\ Background & 0.43 & 0.43 \\ Average & 0.39 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: mIoU of original and distortion correction data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Original Data** & **Distortion Correction Data** \\ \hline Walls & 0.52 & 0.69 \\ Windows & 0.27 & 0.53 \\ Entrances & 0.338 & 0.50 \\ Background & 0.43 & 0.43 \\ Average & 0.39 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: mIoU of original and distortion correction data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Original Data** & **Distortion Correction Data** \\ \hline Walls & 0.52 & 0.69 \\ Windows & 0.27 & 0.53 \\ Entrances & 0.338 & 0.50 \\ Background & 0.43 & 0.43 \\ Average & 0.39 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Pixel accuracy of original and distortion correction data. Table 16 shows the accuracy of the facade information obtained by the output and ground truth images. The accuracy of the facade information of the distorted and corrected data was lower than that of the CMP database by 0.09, but it was higher by 0.07, 0.04, and 0.09 in the secondary color, number of floors, respectively, and the number of buildings was 0.03 higher in the accuracy of the average facade information. This is a marginally different result from the mIoU and pixel accuracy values and is assumed to be the result of the facade characteristics of Dongsung-ro being tested. The facade characteristics of Dongsung-ro are as follows. The accuracy of the secondary color calculation was high because the ratio of the area of the window class, which has a relatively high mIoU, is high and the color of the window is often calculated as a secondary color because it is included in the facade.