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Imaging of the Microstructural Failure Mechanism in the Human Hip

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Multi-defect type beam bridge dataset: GYU-DET.

Ruiping Li^1,2, Linchang Zhao^3,4, Hao Wei^1,2

¹School of Computer Science, Guiyang University, Guiyang, 550005, China.

Scientific Data

|July 2, 2025

Summary

This study introduces the GYU-DET dataset for detecting bridge surface defects. This high-quality dataset aids in developing advanced computer vision models for intelligent bridge health monitoring.

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Area of Science:

Computer Vision
Civil Engineering
Artificial Intelligence

Background:

Existing datasets for bridge defect detection lack scale, annotation accuracy, and environmental diversity.
Accurate identification of bridge surface defects is crucial for structural integrity and safety.

Purpose of the Study:

To introduce the GYU-DET dataset, a comprehensive resource for bridge surface defect detection.
To address limitations in existing datasets regarding scale, annotation accuracy, and environmental diversity.

Main Methods:

The GYU-DET dataset comprises 11,123 high-resolution images covering six defect types (cracks, spalling, seepage, honeycomb surface, exposed rebar, holes).
Images capture diverse lighting and environmental conditions across various bridge structural parts.
Annotations adhere to strict guidelines and are provided in YOLO format for computer vision tasks.

Main Results:

Experiments using the YOLOv11 object detection model validated the dataset's effectiveness.
The GYU-DET dataset demonstrated its capability to support bridge defect detection tasks.

Conclusions:

The GYU-DET dataset provides high-quality data essential for advancing bridge surface defect detection.
This resource promotes the development of intelligent bridge health monitoring technologies through improved computer vision models.