Achieving intelligent and automated detection of defects in wind turbine blades has become a critical task for contemporary wind farm inspection operations. However, existing datasets for blade defect detection exhibit notable shortcomings, including insufficient defect attributes and limited scale, which hinder the advancement of related detection algorithms. This paper presents a standardized multiclass dataset of visible images of wind turbine blade defects for visual inspection, comprising six categories and 1,065 real blade images captured by unmanned aerial vehicles (UAVs). To provide a comprehensive characterization of this dataset, we conducted a feature space analysis using t-SNE to identify unique attributes of the defective targets. The dataset addresses the lack of diverse defect types and high-resolution samples in existing resources, providing a benchmark for the development of visual inspection algorithms.
Ji et al. (2026) studied this question.