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

David Abou Chacra, Henry Leopold, Jeremy Pinto, Norman Lunscher, Georges Younes, John Zelek

Abstract


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 algorithm 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 a municipal level.


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DOI: http://dx.doi.org/10.15353/vsnl.v2i1.94

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