# Multiple data sources and domain generalization learning method for road surface defect classification

arXiv: https://arxiv.org/abs/2407.10197 · HTML: https://mlsanity.com/paper/2407.10197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 14 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification](https://mlsanity.com/paper/2012.00417/) (2020)
- [Learning Meta Face Recognition in Unseen Domains](https://mlsanity.com/paper/2003.07733/) (2020)
- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Global Road Damage Detection: State-of-the-art Solutions](https://mlsanity.com/paper/2011.08740/) (2020)
- [Assessment of Deep Convolutional Neural Networks for Road Surface Classification](https://mlsanity.com/paper/1804.08872/) (2018)
- [Cross-Domain Similarity Learning for Face Recognition in Unseen Domains](https://mlsanity.com/paper/2103.07503/) (2021)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [Domain-aware Triplet loss in Domain Generalization](https://mlsanity.com/paper/2303.01233/) (2023)
- [FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images](https://mlsanity.com/paper/2309.03799/) (2023)

## Built on by (most cited first, 2 of 2)

- [Data selection method for assessment of autonomous vehicles](https://mlsanity.com/paper/2407.12065/) (2024)
- [Improving classification of road surface conditions via road area extraction and contrastive learning](https://mlsanity.com/paper/2407.14418/) (2024)

## Abstract (arXiv)

Roads are an essential mode of transportation, and maintaining them is critical to economic growth and citizen well-being. With the continued advancement of AI, road surface inspection based on camera images has recently been extensively researched and can be performed automatically. However, because almost all of the deep learning methods for detecting road surface defects were optimized for a specific dataset, they are difficult to apply to a new, previously unseen dataset. Furthermore, there is a lack of research on training an efficient model using multiple data sources. In this paper, we propose a method for classifying road surface defects using camera images. In our method, we propose a scheme for dealing with the invariance of multiple data sources while training a model on multiple data sources. Furthermore, we present a domain generalization training algorithm for developing a generalized model that can work with new, completely unseen data sources without requiring model updates. We validate our method using an experiment with six data sources corresponding to six countries from the RDD2022 dataset. The results show that our method can efficiently classify road surface defects on previously unseen data.