URDD: An open dataset for urban roadway disease detection and classification

Published: 20 February 2025| Version 2 | DOI: 10.17632/r7pnxpr2bb.2
Contributors:
,
,
, Lei Ren, Chunbo Li,

Description

We present two urban road disease datasets: DURDD for road disease detection and CURDD for road disease classification. DURDD includes four main types of underground road diseases: cavity, detachment, water-rich, and looseness. It also contains disease detection datasets in three base formats: COCO, Pascal VOC, and YOLO. In CURDD, the dataset is divided into two levels: level 0 and level 1, corresponding to the "Cls0" and "Cls1" catalogs, respectively. Level 1 includes cavity, detachment, water-rich, looseness, and background. Level 0 categories combine the four main disease types mentioned earlier into a single "diseases" category, with the other category being "background." This dataset was jointly published by Hebei University and the 519 Team of North China Geological Exploration Bureau. We support individuals or teams using the data for research purposes. We also welcome collaboration for commercial use. For commercial inquiries, please contact us for authorization.

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Institutions

Hebei University

Categories

Earth Sciences, Computer Vision, Use of Computers in Earth Sciences

Funders

  • Funded by S&T Program of Hebei Province
    Grant ID: 246Z0104G
  • Science Foundation Science Research Project of Hebei Province
    Grant ID: CXY2024031
  • Hebei University Research and Innovation Team Support Project
    Grant ID: IT2023B05

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