FLOOR SLAB CACK SEGMENTATION LERANING FOR INSPECTION AND NUMERICAL DETERIORATION INDICATOR

Abstract

Among the deterioration of 700,000 bridges in Japan, making the bridge inspection once every five years sustainable is a fundamental issue. In order to speed up the cycle from inspection to repair measures, it is required to inspect consistently health condition while suppressing variations, and to select a repair method using numerical indicators. In order to extract the pixel-wise region of bridge elements, we propose a method that learns a target region detector useful for feature extraction of damage by semantic segmentation using the dataset for the images of the floor slab and its crack annotated labels. This method automatically calculates a deterioration index for scaling crack area．Finally, we address the issue of generalization of bridge inspection support tools.