Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps

Abstract

Road quality assessment is a crucial part in municipalities’ workto maintain their infrastructure, plan upgrades, and manage theirbudgets. Properly maintaining this infrastructure relies heavily onconsistently monitoring its condition and deterioration over time.This can be a challenge, especially in larger towns and cities wherethere is a lot of city property to keep an eye on. We review roadquality assessment methods currently employed, and then describeour novel algorithm aimed at identifying distressed road regionsfrom street view images and pinpointing cracks within them. Wepredict distressed regions by computing Fisher vectors on localSIFT descriptors and classifying them with an SVM trained to distinguishbetween road qualities. We follow this step with a comparisonto a weighed contour map within these distressed regionsto identify exact crack and defect locations, and use the contourweights to predict the crack severity. Promising results are obtainedon our manually annotated dataset, which indicate the viability ofusing this cost-effective system to perform road quality assessmentat a municipal level.