Ren Miao, Zhang Xianfeng, Zhi Xiaobo, Wei Yuanjia, Feng Ziyuan
Institute of Remote Sensing and Geographic Information System, Peking University, 5 Summer Palace Road, Beijing, 100871, China.
Sci Data. 2024 Apr 22;11(1):407. doi: 10.1038/s41597-024-03263-7.
Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images.
道路损坏对道路的使用寿命和安全构成巨大威胁，而早期检测路面损坏有助于进行维护和修复。街景图像因其广泛的覆盖范围和定期更新，成为监测路面损坏的新解决方案。在本研究中，利用从百度地图获取的8000张街景图像，开发了一个道路路面损坏数据集，即用于自动道路损坏检测的街景图像数据集（SVRDD）。基于这些图像，通过视觉识别并标注了超过20000个损坏实例。这些实例分布在北京的五个行政区。使用SVRDD数据集对十种成熟的目标检测算法进行了训练和评估。结果展示了这些算法在检测路面损坏方面的性能。据我们所知，SVRDD是第一个基于街景图像的用于路面损坏检测的公共数据集。它可以为基于街景图像的深度学习算法的未来发展提供可靠的数据支持。
