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. 2025 Jul 1;12(1):1101.
doi: 10.1038/s41597-025-05395-w.

Multi-defect type beam bridge dataset: GYU-DET

Ruiping Li #  1   2 , Linchang Zhao #  3   4 , Hao Wei #  1   2 , Guoqing Hu #  5 , Yongchi Xu #  6 , Bocheng Ouyang #  1 , Jin Tan #  7
Affiliations

Multi-defect type beam bridge dataset: GYU-DET

Ruiping Li et al. Sci Data. .

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.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Sample of bridge objects.
Fig. 2
Fig. 2
Visual examples of annotation cases in the GYU-DET dataset.
Fig. 3
Fig. 3
GYU-DET Annotation Process.
Fig. 4
Fig. 4
Comparison of the number of instances for all defect types in GZ-DET.
Fig. 5
Fig. 5
Images under different lighting and environmental conditions.
Fig. 6
Fig. 6
Annotation of the bridge surface defects in “260.jpg” in GYU-DET.
Fig. 7
Fig. 7
YOLOv11 Training Results.
Fig. 8
Fig. 8
Results of validation using the test set.
Fig. 9
Fig. 9
Detection results of the test dataset in GYU-DET.
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