In the emerging field of urban digital twins (UDTs), there are extensive and captivating opportunities for leveraging cutting-edge deep learning techniques. 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 suite 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.


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    Title :

    UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite


    Contributors:
    Guo, Sicen (author) / Li, Jiahang (author) / Feng, Yi (author) / Zhou, Dacheng (author) / Zhang, Denghuang (author) / Chen, Chen (author) / Su, Shuai (author) / Zhu, Xingyi (author) / Chen, Qijun (author) / Fan, Rui (author)


    Publication date :

    2024-08-01


    Size :

    12837683 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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