Estimation of Corrosion Area of Bridge Attached Pipeline Using Instance Segmentation

Abstract

Methods using image recognition are effective in improving the efficiency of infrastructure facility in- spection work. In this paper, we propose a method for estimating the corroded area of steel bridge girder conduits and girder members, which are communication facilities, from images. The proposed method uti- lizes instance segmentation, a deep learning method, to detect the areas of bridge girder conduits and girder members individually in the image and then applies semantic segmentation to detect the areas of corrosion that have occurred. The percentage of corrosion area for each structure is then calculated on the basis of the results. The correlation coefficient between the actual corrosion area ratio and the estimated value of the proposed method is 0.976, which is accurate enough to determine the progress of corrosion in a structure.