A STUDY ON CORROSION AREAS CALCULATION FOR STEEL GIRDER BRIDGES APPLYING IMAGE BINARIZATION TO CONVOLUTIONAL NEURAL NETWORK OUTPUTS

Abstract

As the damage of bridges is expected to accelerate, there is a need for maintenance and management based on an efficient inspection. The Convolutional Neural Network (CNN) is a type of the machine learning, which is one of the most effective methods to support the inspection of steel girder bridges. However, the computational cost of a pixel-by-pixel corrosion detection such as the semantic segmentation method is high, and it is difficult for inspection engineers to use that method in the inspection site. Therefore, the purpose of this study is to propose our method that allows inspection engineers to detect a corrosion and its area at short times in the inspection site. A series of methods in this study were which examined, we first performed the corrosion detection using the CNN, and then the corrosion area calculation applied the image binaization to the output of the CNN.