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/).

许多市政当局和道路当局寻求实施道路损坏的自动评估。然而，他们往往缺乏技术、专业知识和资金来购买最先进的设备来收集和分析道路损坏的数据。尽管一些国家（如日本）已经开发出成本较低且易于使用的基于智能手机的自动道路状况监测方法，但其他国家仍在努力寻找有效的解决方案。这项工作在这方面做出了以下贡献。首先，它评估了日本模型在其他国家的可用性。其次，它提出了一个大规模异构道路损坏数据集，包括使用智能手机从多个国家（印度、日本和捷克共和国）收集的 26,620 张图像。第三，它提出了能够在一个以上国家检测和分类道路损坏的模型。最后，当其他国家/地区发布其自动道路损坏检测和分类数据和模型时，该研究可为其他国家/地区的读者、地方机构和市政当局提供建议。提议的数据集的一部分用于 2020 年全球道路损坏检测挑战赛，可以在 (https://github.com/sekilab/RoadDamageDetector/) 上访问。