Fully Automated Road Defect Detection Using Street View Images

Road quality assessment is a crucial part in municipalities' work to maintain their infrastructure, plan upgrades, and manage their budgets. Properly maintaining this infrastructure relies heavily on consistently monitoring its condition and deterioration over time. This can be a challenge, especially in larger towns and cities where there is a lot of city property to keep an eye on. We review road quality assessment methods currently employed, and then describe our novel system, which integrates a collection of existing algorithms, aimed at identifying distressed road regions from street view images and pinpointing cracks within them. We predict distressed regions by computing Fisher vectors on local SIFT descriptors and classifying them with an SVM trained to distinguish between road qualities. We follow this step with a comparison to a weighed contour map within these distressed regions to identify exact crack and defect locations, and use the contour weights to predict the crack severity. Promising results are obtained on our manually annotated dataset, which indicate the viability of using this cost-effective system to perform road quality assessment at the municipal level.