  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.
