UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
| Autor: | Guo, Sicen, Li, Jiahang, Feng, Yi, Zhou, Dacheng, Zhang, Denghuang, Chen, Chen, Su, Shuai, Zhu, Xingyi, Chen, Qijun, Fan, Rui |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtiri |
| Databáze: | arXiv |
| Externí odkaz: |
|