URDD: An open dataset for urban roadway disease detection and classificationMendeley Data
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accidents have become more common in recent years due to the rising number of motorized vehicles, climate change, and outdated subsurface drainage systems.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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925002318 |
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| author | Shuaiqi Liu Wenjing Jiang Yue Yu Lei Ren Chunbo Li Qi Hu |
| author_facet | Shuaiqi Liu Wenjing Jiang Yue Yu Lei Ren Chunbo Li Qi Hu |
| author_sort | Shuaiqi Liu |
| collection | DOAJ |
| description | 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accidents have become more common in recent years due to the rising number of motorized vehicles, climate change, and outdated subsurface drainage systems.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. |
| format | Article |
| id | doaj-art-5a03b0d5c242427da6402adc41ccb64c |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-5a03b0d5c242427da6402adc41ccb64c2025-08-20T03:25:04ZengElsevierData in Brief2352-34092025-06-016011149910.1016/j.dib.2025.111499URDD: An open dataset for urban roadway disease detection and classificationMendeley DataShuaiqi Liu0Wenjing Jiang1Yue Yu2Lei Ren3Chunbo Li4Qi Hu5College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China; Corresponding authors at: College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, ChinaCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China519 Team of North China Geological Exploration Bureau, China519 Team of North China Geological Exploration Bureau, ChinaCollege of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China; Corresponding authors at: College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.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.http://www.sciencedirect.com/science/article/pii/S2352340925002318Ground-penetrating radarRoad diseaseObject detectionClassificationDeep learning |
| spellingShingle | Shuaiqi Liu Wenjing Jiang Yue Yu Lei Ren Chunbo Li Qi Hu URDD: An open dataset for urban roadway disease detection and classificationMendeley Data Data in Brief Ground-penetrating radar Road disease Object detection Classification Deep learning |
| title | URDD: An open dataset for urban roadway disease detection and classificationMendeley Data |
| title_full | URDD: An open dataset for urban roadway disease detection and classificationMendeley Data |
| title_fullStr | URDD: An open dataset for urban roadway disease detection and classificationMendeley Data |
| title_full_unstemmed | URDD: An open dataset for urban roadway disease detection and classificationMendeley Data |
| title_short | URDD: An open dataset for urban roadway disease detection and classificationMendeley Data |
| title_sort | urdd an open dataset for urban roadway disease detection and classificationmendeley data |
| topic | Ground-penetrating radar Road disease Object detection Classification Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925002318 |
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