Multi-scale deep convolutional neural networks for evaluating cracks on road pavement in highways

Abstract

In highways in Japan, various quantities are measured during inspections for maintenance and manage- ment using specialized road condition measurement vehicles while driving. In particular, for pavement surfaces, high-resolution and continuous images are captured using line-scan cameras, and these images are visually inspected to assess the condition with respect to cracks. In this study, we propose an analysis system for evaluating pavement condition related to cracking using images obtained from a road condition measurement vehicle. Specifically, we sequentially apply four neural networks to pavement images at dif- ferent scales to achieve an evaluation method consistent with crack assessment rules in highways. After constructing these networks and their application method, we calculate the crack ratio and compare it with the values obtained by expert visual inspections to examine the validity of the proposed method.