使用无人机图像智能检测风力轮机叶片缺陷的多类数据集
Lipeng Ji1, Junjie Cheng2, Shilong Wu2,3
1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. jilipeng@usst.edu.cn.
Scientific data
|February 9, 2026
概括
创建了1065个风力轮机叶片缺陷图像的新数据集,以改进自动缺陷检测. 这种标准化资源解决了现有数据集的局限性,有助于开发先进的检查算法.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 风力轮机叶片的自动缺陷检测对于高效的风电场运营至关重要.
- 现有的数据集缺乏足够的缺陷属性和规模,阻碍了算法开发.
- 使用无人机 (UAV) 进行视觉检查是叶片评估的关键方法.
研究的目的:
- 为风力轮机叶片缺陷检测引入标准化,多类数据集.
- 提供一个基准数据集,解决现有资源的缺陷.
- 促进智能和自动缺陷检测算法的发展.
主要方法:
- 使用无人机收集了1065张真正的风力轮机叶片缺陷的高分辨率图像.
- 将缺陷分类为六个不同的类别,用于多类分析.
- 使用t分布式静态邻近嵌入 (t-SNE) 进行特征空间分析以描述缺陷.
主要成果:
- 数据集包括6个缺陷类别的1,065张图像.
- 功能空间分析揭示了不同缺陷类型的独特属性.
- 与以前的资源相比,数据集在缺陷类型和样本解决方案方面提供了更大的多样性.
结论:
- 本文所介绍的数据集是开发和评估风力轮机叶片视觉检查算法的宝贵基准.
- 这一资源将加速自动缺陷检测的进展,增强风电场维护.
- 数据集的标准化性质和全面的表征支持了强大的算法开发.
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