# Improving classification of road surface conditions via road area extraction and contrastive learning

arXiv: https://arxiv.org/abs/2407.14418 · HTML: https://mlsanity.com/paper/2407.14418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Assessment of Deep Convolutional Neural Networks for Road Surface Classification](https://mlsanity.com/paper/1804.08872/) (2018)
- [The Second Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2304.07051/) (2023)
- [FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images](https://mlsanity.com/paper/2309.03799/) (2023)
- [SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles](https://mlsanity.com/paper/2311.11580/) (2023)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)
- [Data selection method for assessment of autonomous vehicles](https://mlsanity.com/paper/2407.12065/) (2024)

## Built on by (most cited first, 1 of 1)

- [Intelligent Road Condition Monitoring using 3D In-Air SONAR Sensing](https://mlsanity.com/paper/2603.28141/) (2026)

## Abstract (arXiv)

Maintaining roads is crucial to economic growth and citizen well-being because roads are a vital means of transportation. In various countries, the inspection of road surfaces is still done manually, however, to automate it, research interest is now focused on detecting the road surface defects via the visual data. While, previous research has been focused on deep learning methods which tend to process the entire image and leads to heavy computational cost. In this study, we focus our attention on improving the classification performance while keeping the computational cost of our solution low. Instead of processing the whole image, we introduce a segmentation model to only focus the downstream classification model to the road surface in the image. Furthermore, we employ contrastive learning during model training to improve the road surface condition classification. Our experiments on the public RTK dataset demonstrate a significant improvement in our proposed method when compared to previous works.