## 5 Demonstration Experiment and Position Identification in Actual Headrace Tunnel
### Analysis Results  
Figures 8-13 show the results of the trained model for detecting chalking positions. In each figure, the blue frames indicate the annotation results, the pink frames indicate the detection results, and the numbers shown above the pink frames indicate the confidence scores of the detection results. Figure 7: Removing mussels. Figure 8 shows an example of detection near a ladder. Almost all the chalking positions were detected, and the confidence score was high, with an average value of 0.845. The detection results (pink frames) had almost the same shape as the blue frames indicating the annotation positions, and both the rectangular areas surrounding the deterioration positions and the characters indicating the information could be detected. In the upper part of Figure 8, there is a ladder on the wall, and only half of the chalked area is visible in the upper left part of this ladder. However, the confidence score is as high as 0.803, and the IoU is as high as 0.866. The IoU, which is described in detail later in the paper, indicates the degree of overlap of the regions and is expressed as a value between 0 and 1. A value  
Figure 8: Example of chalking detection 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. Almost all the chalking positions were detected with high confidence scores. closer to 1 indicates a higher accuracy. As shown in Figure 8, almost all chalked areas were detected without significant deviations. This was confirmed by the fact that the mean IoU (mIoU), i.e., the average value of the IoU in Figure 8, was 0.840. Similarly, in Figure 9, most of the damage positions were detected, and the confidence score was high, with an average value of 0.871. However, at one position near the bottom of Figure 9, the confidence score was 0.149. Most of the chalking positions in this study had square-like shapes with similar aspect ratios, but there were a few chalking positions with a vertically long rectangular shape located at the center of Figure 9. This chalking point has not only a high confidence score of 0.817 but also a high IoU of 0.826. The developed model detected these few characteristic points with a high confidence score and IoU. Similarly, the mIoU in Figure 9 was as high as 0.739. In Figure 9, there are two yellow chalking points slightly above the aforementioned vertical chalking points. The accuracy was increased by the fact that the yellow chalked areas were not detected incorrectly, because only the white chalked area was the target in this study. Figure 9: Example of chalking detection 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. Detection was achieved for not only chalking positions with square-shaped areas having similar aspect ratios but also shapes that did not exist in large numbers, such as vertically or horizontally long shapes. The yellow chalked area in the upper center part was not detected, and it can be said that the color was recognized. In the case of Figure 10, similar to Figures 8 and 9, the positions were detected with high accuracy, but there were positions where only the numbers and symbols that were chalked were detected incorrectly. These numbers and symbols were written using the same chalk as those in the annotations, and their sizes were very similar; thus, it is possible that such misdetection occurred. However, these detection results had very low confidence scores, with values of 0.173 and 0.179, as shown in Figure 10. Herein, all the results are presented, regardless of the confidence score; thus, there are false positives such as this. We consider that setting a threshold for the confidence score will increase the accuracy. However, even with these false detections, the mIoU was as high as 0.756, indicating that the detection was highly accurate.