## 7 Conclusions  
We sought to increase the efficiency of detecting deformation positions on the inner walls of headrace tunnels by developing a continuous nondestructive survey system for headraces that captures wall surfaces in headrace tunnels, and we used continuous images of the wall captured with this photography device to detect chalking positions on the inner wall of the North Chiba Headrace using YOLOv7. Our findings are summarized below. * The model developed in this study allowed us to determine the location of deterioration with an accuracy as high as 99.02%. * Chalking detection from continuous wall images allows quantitative and qualitative evaluation of the total number and distribution of deteriorated areas, facilitating the identification of changes over time and the factors that cause deterioration phenomena. * The cost and time associated with investigation and diagnosis are reduced by approximately JPY 13.74 million/year and one-fifth, respectively, by using the developed continuous nondestructive survey system. * Effective maintenance and management can be achieved through the acquisition of data that can be easily stored in a database and the development of a series of systems to monitor the deterioration status. * The continuous wall surface images and the chalking locations detected using the images are recorded, plotted, and stored in a database, leading to an advanced asset-management system for headrace tunnels. We were able to significantly reduce the time required for inspection by using this device to photograph the inside of the headrace tunnel, making efficient maintenance and management possible. Additionally, we recorded and saved the state of the wall surface as continuous images, which made it easier to identify and tabulate the deterioration positions and to follow the changes over time. By capturing continuous images of the headrace tunnel wall and determining the chalking positions, we significantly reduced the cost of inspection and recording, which previously relied on visual inspection. Furthermore, it is expected that the distribution and characteristics of the deterioration positions will clarify the causes of deterioration and facilitate the proposal of repair plans that match these characteristics and trends. Additionally, the creation of a database of the location, type, and extent of the deterioration that is discovered at the time of inspection will lead to the establishment of a deterioration prediction method that can be utilized for future inspection and repair planning as well as a reduction in lifecycle costs.Although researchers have conducted inspections and surveys of the inner walls of headrace tunnels using robots, no previous studies have been carried out on the detection of chalked areas by AI using continuous wall surface images captured via the surveys using nondestructive systems based on CCD line cameras and quantitative and qualitative evaluation of the inspection and survey results, making the present study unique. The proposed system allows efficient maintenance and management of headrace tunnels. As discussed in a previous section, the number and distribution of chalking points (which corresponded to corrosion points in this study) in each headrace tunnel were determined using the captured images. From the high detection accuracy and recall, we concluded that the actual number and distribution of chalking points were similar to the detection results, indicating that the developed system can be used to maintain and manage headrace tunnels. The data used for training and validation in this study were obtained from different areas of the tunnel, and the wall surfaces were in different conditions; thus, the developed model was validated. However, we believe that it is necessary to check whether similar detection performance can be achieved for headrace tunnels with different characteristics. Additionally, the damage severity was not discussed in this study, and we wish to be able to determine it from distribution trends and other factors.