MULTI-TYPE BRIDGE DAMAGE DETECTION METHOD BASED ON YOLO

Abstract

In this study, we try to solve the problem by using UAV and YOLO, which is one of the deep learning methods, for bridge inspection work, which is concerned about labor shortage and cost. By using deep learning, it is possible to shorten the work time by programming the work and suppress oversights and mistakes by adding an objective element to the diagnosis of damage. In the verification using CNN conducted by Tabata et al. As a previous study, the damaged part could be roughly recognized by the damage recognition of the image taken by the UAV, but the background part that had not been trained was mistakenly recognized as the damage. In addition, the UAV video diagnosis took a long time to detect, making it unsuitable for practical use. For these problems, we will verify using the YOLO v3 model, which is resistant to false detection of the background and can perform detection at high speed.