Quantifying Wind Turbine Blade Surface Roughness using Sandpaper Grit Sizes: An Initial Exploration

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Surface inspection of wind turbine blades is a necessary step, to ensure longevity and sustained high energy output. The detection of accumulation of damages and increased surface roughness of in-use blades, is one of the main objectives of inspections in the wind energy industry. Creating 3D scans of the leading edges of blade surfaces has been more and more used for capturing the roughness profile of blades. An important part in analysing these surface 3D scans is the standardization of the captured data across different blade surfaces, types and sizes. In this paper we propose an initial exploration of using sandpaper grit sizes to provide this standardization. Sandpaper has been widely used for approximating different levels of blade surface roughness and its standardized nature can be used to easily describe and compare blade surfaces. We reconstruct a number of different sandpaper grit sizes - from coarser P40 to a finer P180. We extract a number of 3D surface features from them and use them to train a random forest classification method. This method is then used to segment the surfaces of wind turbine blades in areas of different surface roughness. We test our proposed solution on a variety of blade surfaces - from smooth to course and damaged and show that it manages to classify them depending on their roughness
Original languageEnglish
Title of host publication16th International Conference on Computer Vision Theory and Applications (VISAPP)
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch
Number of pages8
PublisherSCITEPRESS Digital Library
Publication date2021
ISBN (Print)978-989-758-488-6
ISBN (Electronic)9789897584886
Publication statusPublished - 2021
Event16th International Conference on Computer Vision Theory and Application - Online Streaming
Duration: 8 Feb 202110 Feb 2021


Conference16th International Conference on Computer Vision Theory and Application
LocationOnline Streaming
Internet address


  • Digital Photography
  • Machine Learning
  • Random Forests
  • 3D Reconstruction
  • Segmentation
  • Wind Turbine Blade Inspection
  • Point clouds
  • Sandpaper roughness
  • Geometrical properties
  • Random forests
  • 3D reconstruction
  • Classification
  • Surface inspection


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