## 3 Analysis on Real Roads and GIS Plotting of Evaluation Results
### Pavement Damage Evaluation and GIS plotting  
First, the model developed in Section 2.2 (Step 9) was used to detect cracks. The results are shown in Figure 11. Two results are shown for each of the six images. In the images on the left, the areas framed in red are those where cracks were correctly detected, while the areas framed in yellow are those where cracks were present but not judged to be present, and the areas framed in green are those where cracks were absent but judged to be present. For better understanding of the figure, we do not overlap the images when dividing them, but simply divide them into 256 \(\times\) 256 segments as in Figure 7. The figure on the right shows the results of the analysis as shown in the middle figure of Figure 9. Note that the analysis was performed with overlapping images, as explained in Figure 4.  
The results A and B are examples of properly detected cracks; the results A and B have different brightness levels, but are properly detected. Result C is a case in which there was a false positive detection, but in reality the classification is not necessarily wrong, because the image is not easy to determine whether there are thin cracks around the manhole or not. In other words, even the inspector is not sure whether it is correct to conclude that there are cracks or not around the manhole. In the six-class classification shown in Table 1, the classifier judges that the image is classified as class 5. In other words, the classifier recognizes that there is both a manhole and a thin crack in the image. Result D was concluded by the classifier to have cracks in the white area, even though there were no cracks. It is easy to mistakenly detect cracks where there are white lines. Result E is an example where the classifier misidentified a thin shadow as a crack. These thin shadows can easily be mistaken as cracks because cracks are characterized by their dark and thin nature. Result F is an example of overlooking cracks that are overlapping shadows, in addition to false positives like Result E. In order to reduce such errors, it would be necessary to train the classifier with more cases like these, but we could not do so because there were not many of them in the data we collected. Nevertheless, the overall accuracy of the crack detection was very high and was sufficient for the purpose of pavement inspection. Then, the crack detection results obtained here were evaluated and assigned three grades according to Equation (6). The results are shown in Table 3. We obtained 94.3% accuracy, which indicates that there is a certain degree of agreement between visual inspection and model judgment. In addition, the model showed that less than 1% (5/601) of grade 3 cases were misjudged as grade 1, indicating that the probability of overlooking significant damage is very small. Figure 10: Road Photography with GMS3. The images we used in the project are linked to the longitude/latitude and easily visualized on GIS. Figure 12 shows the results of visual inspection obtained by following the Japanese pavement inspection procedure and evaluation by our method on GIS. A wide area view and an enlarged view are shown. As shown in the figure, our method replicated the visual inspection results with satisfactory accuracy. In particular, if we look at the enlarged view, we can see that the damage is more frequent at intersections, and this may be due to the large external forces acting on them, such as braking. As shown above, the system proposed in this paper, which automates the plotting of pavement conditions on a GIS, is accurate and can contribute to tremendous labor saving in pavement inspection. Figure 11: Crack detection results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Visual InspectionAnalysis Result** & **Grade 1** & **Grade 2** & **Grade 3** \\ \hline Grade 1 & 47,058 & 2835 & 132 \\ \hline Grade 2 & 309 & 7773 & 25 \\ \hline Grade 3 & 5 & 34 & 562 \\ \hline \end{tabular}
\end{table} 
Table 3: A confusion matrix with three grades.