It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset.

翻译：城市数字孪生这一新兴领域在利用强大的深度学习方法方面具有巨大潜力，尤其在智能道路检测领域，目前相关研究和可用数据仍有限。为促进该领域发展，我们构建了一个标注完善的道路坑洞数据集，命名为城市数字孪生智能道路检测（UDTIRI）数据集。我们希望该数据集能助力在道路检测中应用强大的深度学习方法，为算法提供更全面的场景理解，充分发挥其潜力。该数据集包含1000张在不同光照和湿度条件下采集的坑洞图像，旨在用于目标检测、语义分割和实例分割任务。团队投入大量精力进行了详细的统计分析，并对近年来具有代表性的算法进行了基准测试。此外，我们还提供了一个多任务平台，供研究者在UDTIRI数据集支持下充分挖掘各类算法的性能。