# Road surface detection and differentiation considering surface damages

arXiv: https://arxiv.org/abs/2006.13377 · HTML: https://mlsanity.com/paper/2006.13377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Damage detection using in-domain and cross-domain transfer learning](https://mlsanity.com/paper/2102.03858/) (2021)
- [Certified Robust Control under Adversarial Perturbations](https://mlsanity.com/paper/2302.02208/) (2023)
- [SmartRSD: An Intelligent Multimodal Approach to Real-Time Road Surface Detection for Safe Driving](https://mlsanity.com/paper/2406.10128/) (2024)
- [A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads](https://mlsanity.com/paper/2411.16295/) (2024)
- [A New Dataset and Framework for Robust Road Surface Classification via Camera-IMU Fusion](https://mlsanity.com/paper/2601.20847/) (2026)

## Abstract (arXiv)

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.