2021 Volume 2 Issue J2 Pages 251-260
Many mortar-sprayed slopes were constructed during the period of High economic growth, and they are aging at the same time. It is difficult to visually check for floats among the abnormalities of mortar-sprayed slopes, so percussion inspections are used to check for them. However, due to the shortage of inspection engineers and the financial difficulties of the national and local governments, there is a limit to the amount of time that can be spent on continuous diagnosis of floats using only percussion inspections. In this study, we developed a deep learning model using infrared images acquired from an infrared camera mounted on a UAV, taking advantage of the difference in heat capacity between a floating part and a sound part. As a result of experiments using images that were not used for learning, it was confirmed that the model could accurately estimate the floating areas on the mortar-sprayed slope.
