Evidence map›Paper›PMID 41663407›Full record

ArticleScientific data2026

Multiclass Dataset for Intelligent Detection of Wind Turbine Blade Defects Using Drone Imagery.

Lipeng Ji, Junjie Cheng, Shilong Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lipeng JiSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. jilipeng@usst.edu.cn.ORCID http://orcid.org/0000-0001-5802-6930
Junjie ChengSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Shilong WuSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Identifiers

PMID41663407
PMCPMC12996307

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.