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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    Titel :

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


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


    Erscheinungsdatum :

    01.08.2024


    Format / Umfang :

    12837683 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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