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

Shuaiqi Liu, Wenjing Jiang, Yue Yu, Lei Ren, Chunbo Li, Qi Hu

Abstract read

In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shuaiqi LiuCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China.

Wenjing JiangCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China.

Yue YuCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China.

Lei Ren519 Team of North China Geological Exploration Bureau, China.

Chunbo Li519 Team of North China Geological Exploration Bureau, China.

Qi HuCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban traffic accidents have become more common in recent years due to the rising number of motorized vehicles, climate change, and outdated subsurface drainage systems. Traditional road disease detection methods involve collecting road data using ground-penetrating radar and manually analyzing the data. This process is time-consuming and subjective. Deep learning, especially convolutional neural networks (CNNs), has proven highly effective in image recognition and object detection. By applying these techniques to road disease detection, both the efficiency and accuracy of detection can be significantly improved. To support this, we have created a specialized road disease dataset designed for object detection and classification tasks. The release of this dataset aims to promote the use of artificial intelligence (AI) in autonomous road disease detection and classification, enhancing detection efficiency and contributing to better urban road maintenance and management.

Indexed as

ClassificationDeep learningGround-penetrating radarObject detectionRoad disease

Identifiers

PMID40226197

DOI10.1016/j.dib.2025.111499

PMCPMC11993165

What OpenQuestion holds

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LicenceCC BY-NC

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.