UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
This work addresses a gap in urban digital twins for road inspection, providing a foundational resource for researchers, though it is incremental as it focuses on dataset creation and benchmarking rather than novel algorithmic advances.
The authors tackled the lack of dedicated datasets and benchmarks for intelligent road inspection in urban digital twins by launching an online open-source benchmark suite called UDTIRI, which includes a road pothole detection task with 1,000 annotated images and evaluates state-of-the-art deep learning networks.
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.