## 5 Demonstration Experiment and Position Identification in Actual Headrace Tunnel
### Analysis Results
The blue frames indicate the annotation results, the pink frames indicate the detection results, and the pink numbers indicate the confidence scores at the time of each detection. Although only two large areas on the left and right were considered as chalking points during annotation, two chalked areas were detected in the left area and three chalked areas were detected in the right area. Since these large areas contain many deteriorated areas (circled areas within a rectangular area), it is not necessarily a mistake to detect more than the annotated data. and three in the right area. For the two areas with confidence scores exceeding 0.800, the rectangular area indicated by the chalk was detected, but for the other areas, only the text was detected. Such large areas include many deteriorated areas (Figure 13, circled areas within chalked areas), and whether the area should be divided into one or two rectangles depends on the judgment of the individual. Therefore, the distribution of the chalked areas should not be judged solely according to the number of chalked areas; rather, the size of the chalked areas should be considered to obtain an accurate grasp of the deterioration status. Furthermore, we evaluated all the detection results using the IoU, which is expressed by Equation (1) and indicates the degree of overlapping of regions. Figure 8:2  
Here, TP, FP, and FN are as shown in Figure 8:3 All these values have units of pixels. This index allows the accuracy of the detection results to be evaluated in pixel units. The IoU for the proposed method was 0.777. Additionally, three accuracy indices--the precision, recall, and F-score--were calculated using the following equations (Equations (2)-(4)). Their values were 0.8066, 0.9902, and 0.8891, respectively. As shown in Figure 6, the overlapping of bounding boxes that indicate damage reduces the value of the above indices, but it is not considered to be a major problem for broadly determining the distribution of damage. Figure 8:4 Figure 8:5 (3) Figure 8:6  
Table 3 presents a breakdown of the detection results, and Table 4 presents each detected and undetected case. As shown in Table 3, there were 609 positions where chalking was detected accurately. As indicated by the first row of Table 4, there were cases of singular and multiple corrosion positions, and these positions were detected even when the shapes were slightly different, e.g., when the rectangular area surrounding them was larger than usual. Only six positions were not detected, and as indicated by the second row of Table 4, these included cases where chalking was performed at seams and dents and where the chalk color was faded. Thus, the results indicated that the proposed model can generally detect objects that are clearly chalked on a flat surface. The number of incorrectly detected positions was 146 and as indicated by the third row in Table 4, there were examples of positions that were chalked in red or yellow and where some numbers or symbols were detected. However, approximately 86% of these were positions where parts of chalking positions or numbers/symbols were detected; thus, it can be said that \"locations with chalking\" were generally captured. The results suggested that the damage positions were generally detected well. Excluding the false detection results, the number of chalking positions detected was 608 out of 614, indicating that the positions were detected with an extremely high accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**True Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**Predicted Class**} & **Positive** & TP (True Positive) & FP (False Positive) \\ \cline{2-4}  & **Negative** & FN (False Negative) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for classification.