In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

翻译：在城市数字孪生（UDT）这一新兴领域，利用尖端深度学习技术的前景广阔且引人注目。特别是在智能道路检测（IRI）这一专业领域中，当前尚存在明显空白，表现为专门研究工作的匮乏以及大规模高质量标注数据集的缺失。为促进该新兴领域的发展，我们推出了名为UDTIRI的在线开源基准测试套件。本文介绍了该套件中的首个在线竞赛——道路坑洼检测任务。该任务提供了一个高质量标注数据集，包含在多种真实场景、不同光照和天气条件下采集的1000张RGB图像及其像素级/实例级真值标注。我们的基准测试对基于卷积神经网络或Transformer开发的最新目标检测、语义分割和实例分割网络进行了系统而全面的评估。预期我们的基准测试将促进先进UDT技术与IRI的融合。通过为算法提供对多样化道路条件的更全面理解，我们期望释放其未开发潜力，并推动这一关键领域的创新。