# Transfer Learning-based Road Damage Detection for Multiple Countries

arXiv: https://arxiv.org/abs/2008.13101 · HTML: https://mlsanity.com/paper/2008.13101/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [AI Driven Road Maintenance Inspection](https://mlsanity.com/paper/2106.02567/) (2021)
- [CNN Model & Tuning for Global Road Damage Detection](https://mlsanity.com/paper/2103.09512/) (2021)
- [Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones](https://mlsanity.com/paper/2202.01421/) (2022)
- [Influence of image noise on crack detection performance of deep convolutional neural networks](https://mlsanity.com/paper/2111.02079/) (2021)

## Abstract (arXiv)

Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones. Thirdly, we propose generalized models capable of detecting and classifying road damages in more than one country. Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. Our dataset is available at (https://github.com/sekilab/RoadDamageDetector/).