Deep learning-based road damage detection and classification for multiple countries

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 usability of Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26,620 images collected from multiple countries (India, Japan, and the Czech Republic) using smartphones. Thirdly, it proposes models capable of detecting and classifying road damages in more than one country. Lastly, the study provides 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. A part of the proposed dataset was utilized for Global Road Damage Detection Challenge'2020 and can be accessed at (https://github.com/sekilab/RoadDamageDetector/).

Automated summary

Many municipalities and road authorities lack the technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. While some countries, like Japan, have developed Smartphone-based methods for automatic road condition monitoring, others still struggle to find efficient solutions. This study assesses usability of the Japanese model for other countries, proposes a large-scale road damage dataset, and models capable of detecting and classifying road damages in multiple countries.