Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing - Nomura, Yasutoshi; Inoue, Masaya; Furuta, Hitoshi (Frontiers in Built Environment, 2022)
In Japan, all bridges should be inspected every five years. Usually, the inspection has been performed through the visual evaluation of experienced engineers. However, it requires a lot of load and expense. In order to reduce the inspection work. an attempt is made in this paper to develop a new inspection method using deep learning and image processing technologies. While using the photos obtained by vehicle-mounted camera, the damage states of bridges can be evaluated manually, it still requires a lot of time and load. To save the time and load, deep learning, which is a method of artificial intelligence is introduced. For image processing, it is necessary to utilize such pre-processing techniques as binarization of pictures and morphology treatment. To illustrate the applicability of the method developed here, some experiments are conducted by using the photos of running surface of concrete bridges of a monorail took by vehicle-mounted camera.