Road surface detection and differentiation considering surface damages

TL;DR

This paper introduces a vision-based method for detecting and differentiating road surfaces and damages, enhancing vehicle navigation safety on varied and damaged roads using low-cost cameras.

Contribution

It presents a novel approach for surface classification and damage detection, including a new ground truth dataset with image segmentation for evaluation.

Findings

Effective detection of paved and unpaved surfaces.

Successful identification of surface damages.

Viability of passive vision with low-cost cameras.

Abstract

A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have as scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surface that may be relevant to driving safety. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.