Multi-defect type beam bridge dataset: GYU-DET
Abstract This paper proposes the GYU-DET dataset for bridge surface defect detection, aiming to address the limitations of existing datasets in terms of scale, annotation accuracy, and environmental diversity. The GYU-DET dataset includes six types of defects: cracks, spalling, seepage, honeycomb su...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05395-w |
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| _version_ | 1849335374585790464 |
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| author | Ruiping Li Linchang Zhao Hao Wei Guoqing Hu Yongchi Xu Bocheng Ouyang Jin Tan |
| author_facet | Ruiping Li Linchang Zhao Hao Wei Guoqing Hu Yongchi Xu Bocheng Ouyang Jin Tan |
| author_sort | Ruiping Li |
| collection | DOAJ |
| description | Abstract This paper proposes the GYU-DET dataset for bridge surface defect detection, aiming to address the limitations of existing datasets in terms of scale, annotation accuracy, and environmental diversity. The GYU-DET dataset includes six types of defects: cracks, spalling, seepage, honeycomb surface, exposed rebar, and holes, with a total of 11,123 high-resolution images. It covers a variety of lighting and environmental conditions, comprehensively reflecting the diversity and complexity of bridge defects. The dataset provides comprehensive coverage of bridge structures, with images covering multiple key structural parts. Strict annotation guidelines ensure annotation accuracy and consistency, using the YOLO format, which facilitates model training and evaluation in computer vision tasks. To validate the effectiveness of the dataset, experiments were conducted using the YOLOv11 object detection model. The results show that GYU-DET can effectively support bridge defect detection tasks in the field of computer vision, providing high-quality data support for bridge surface defect detection tasks and promoting the development of intelligent bridge health monitoring technology. |
| format | Article |
| id | doaj-art-bc38a1c7d1fc42708f6c651ee6e1fde7 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-bc38a1c7d1fc42708f6c651ee6e1fde72025-08-20T03:45:18ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05395-wMulti-defect type beam bridge dataset: GYU-DETRuiping Li0Linchang Zhao1Hao Wei2Guoqing Hu3Yongchi Xu4Bocheng Ouyang5Jin Tan6School of Computer Science, Guiyang UniversitySchool of Computer Science, Guiyang UniversitySchool of Computer Science, Guiyang UniversityPKU-HKUST Shenzhen-Hong Kong Institution, Shenzhen Institute of Peking UniversitySchool of Information Engineering, Guiyang UniversitySchool of Computer Science, Guiyang UniversitySchool of Information Science and Engineering, Chongqing Jiaotong UniversityAbstract This paper proposes the GYU-DET dataset for bridge surface defect detection, aiming to address the limitations of existing datasets in terms of scale, annotation accuracy, and environmental diversity. The GYU-DET dataset includes six types of defects: cracks, spalling, seepage, honeycomb surface, exposed rebar, and holes, with a total of 11,123 high-resolution images. It covers a variety of lighting and environmental conditions, comprehensively reflecting the diversity and complexity of bridge defects. The dataset provides comprehensive coverage of bridge structures, with images covering multiple key structural parts. Strict annotation guidelines ensure annotation accuracy and consistency, using the YOLO format, which facilitates model training and evaluation in computer vision tasks. To validate the effectiveness of the dataset, experiments were conducted using the YOLOv11 object detection model. The results show that GYU-DET can effectively support bridge defect detection tasks in the field of computer vision, providing high-quality data support for bridge surface defect detection tasks and promoting the development of intelligent bridge health monitoring technology.https://doi.org/10.1038/s41597-025-05395-w |
| spellingShingle | Ruiping Li Linchang Zhao Hao Wei Guoqing Hu Yongchi Xu Bocheng Ouyang Jin Tan Multi-defect type beam bridge dataset: GYU-DET Scientific Data |
| title | Multi-defect type beam bridge dataset: GYU-DET |
| title_full | Multi-defect type beam bridge dataset: GYU-DET |
| title_fullStr | Multi-defect type beam bridge dataset: GYU-DET |
| title_full_unstemmed | Multi-defect type beam bridge dataset: GYU-DET |
| title_short | Multi-defect type beam bridge dataset: GYU-DET |
| title_sort | multi defect type beam bridge dataset gyu det |
| url | https://doi.org/10.1038/s41597-025-05395-w |
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