The proposed dataset aims to provide a number of wind turbine blade images for testing and training purposes. The featured datasets can be used for testing and evaluation of Structure from Motion algorithms for 3D reconstruction, as data for machine learning algorithms for detecting damaged areas on blades or for quantifying blade surface roughness. Images are taken using Canon 5Ds DSLR camera with resolution of 8688 x 5792. The images come with EXIF data, containing additional information about the used capturing settings. The dataset is separated into two parts: - A dataset containing 5 wind turbine blade patches of areas of different surface structure. Two of the patches are of damaged areas, two of the patches are of rough areas, without pronounced surface deformations and one of the patches is a reference area, that does not contain any roughness or damages. The final patch contains the whole of the blade's edge area and is comprised of a mix of severely damaged areas, areas of small roughness and clear areas. The dataset also contains ground truth microscopy data for two of the damaged patches. The ground truth is scaled to absolute scale. - A dataset containing images of a small blade segment. The blade has been sand blasted, to imitate prolonged real world use. The images are taken both outdoor and indoor. The indoor images contain patches specifying areas of interest - one containing a rough patch and one containing damages. The outdoor images are focused on the whole blade and do not contain patches. The dataset also contains ground truth microscopy data for the two patches, which is scaled to absolute scale.
Date made available12 Aug 2020
PublisherMendeley Data


  • wind turbine blades
  • 3D reconstruction
  • benchmark
  • microscopy
  • surface inspection
  • SfM

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