Corrosion Damage Detection in Headrace Tunnel Using YOLOv7 with Continuous Wall Images

Abstract

Infrastructure that was constructed during the high economic growth period of Japan is starting to deteriorate; thus, there is a need for the maintenance and management of these structures. The basis of maintenance and management is the inspection process, which involves finding and recording damage. However, in headrace tunnels, the water supply is interrupted during inspection; thus, it is desirable to comprehensively photograph and record the tunnel wall and detect damage using the captured images to significantly reduce the water supply interruption time. Given this background, the aim of this study is to establish an investigation and assessment system for deformation points in the inner walls of headrace tunnels and to perform efficient maintenance and management of the tunnels. First, we develop a mobile headrace photography device that photographs the walls of the headrace tunnel with a charge-coupled device line camera. Next, we develop a method using YOLOv7 for detecting chalk marks at the damage locations made during cleaning of the tunnel walls that were photographed by the imaging system, and these results are used as a basis to develop a system that automatically accumulates and plots damage locations and distributions. For chalking detection using continuous wall surface images, a high accuracy of 99.02% is achieved. Furthermore, the system can evaluate the total number and distribution of deteriorated areas, which can be used to identify the causes of change over time and the occurrence of deterioration phenomena. The developed system can significantly reduce the duration and cost of inspections and surveys, and the results can be used to select priority repair areas and to predict deterioration through data accumulation, contributing to appropriate management of headrace tunnels.