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Updated: May 22, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

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Published on: April 20, 2016

Wind turbine blades surface defect high-quality image dataset construction and performance validation.

Ruihua Zhang1, Sunxin Wang2, Sheng Liu3

  • 1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, 710048, China.

Scientific Data
|May 20, 2026
PubMed
Summary

This study introduces WTBs2025, a comprehensive dataset for wind turbine blade defect detection. It features 7544 images across 9 defect types, enhancing object detection model performance.

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Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Wind turbine blade integrity is crucial for operational efficiency and safety.
  • Accurate detection of surface defects is essential for predictive maintenance and preventing catastrophic failures.
  • Existing datasets may lack diversity in defect types or real-world applicability.

Purpose of the Study:

  • To introduce WTBs2025, a novel and comprehensive dataset for wind turbine blade surface defect detection.
  • To provide a standardized, high-quality resource for training and evaluating object detection models.
  • To address the scarcity of specific defect samples, like lightning strikes, through advanced data augmentation.

Main Methods:

  • Collection and preprocessing of real-world wind turbine blade images from operational wind farms.
  • Implementation of data augmentation techniques, including Generative Adversarial Networks (GANs) like GA-DCGAN, to address limited sample sizes for certain defects.
  • Categorization of defects into 9 distinct types with high-quality annotations.

Main Results:

  • The WTBs2025 dataset comprises 7544 images covering 9 types of wind turbine blade defects.
  • The dataset exhibits diverse defect types, extensive image collection, and robust features like broad coverage and standardized formats.
  • Experiments demonstrate the dataset's effectiveness when used with well-known object detection models, showing promising performance.

Conclusions:

  • WTBs2025 offers significant practical value for the field of wind turbine blade damage object detection.
  • The dataset's compatibility and specificity make it a valuable resource for research and application in defect detection.
  • WTBs2025 facilitates advancements in monitoring and maintaining wind turbine infrastructure.