2023 Volume 79 Issue 22 Article ID: 22-22015
In Japan, the percentage of sewer pipes that have reached the standard service life of 50 years is rapidly increasing. Therefore, there is a need for an efficient maintenance and management methods for sewer pipes. However, the current inspection process is time-consuming and labor-intensive because the surveyor visually checks for abnormalities in the images of the sewer pipe taken by CCTV camera. Furthermore, the surveyor determines the degree of damage based on experience, making uniform and quantitative evaluation difficult. In this research, we propose a method to detect damage such as cracks and breaks and to determine the degree of damage by using deep learning to analyze video images taken of sewer pipes. This enables quantitative evaluation of the degree of damage and contributes to reduction of labor requirements and to the advancement of inspection work. We confirmed the usefulness of the proposed method through empirical analysis.
