Automatic Defect Detection in Wind Turbine Blade Images: Model Benchmarks and Re-Annotations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic detection of defects from wind turbine blade images has shown tremendous progress in recent years. However, there are not many annotated datasets feasible for benchmarking purposes, and a lack of consistency in annotation procedures across existing works. In this paper, we investigate the data annotation process for wind turbine blade images to reduce inaccuracies in defect detection and to benchmark the performance of the patch-based detection framework on recent deep learning architectures. In this study, we identify challenges in the detection task that are incurred by the presence of extreme bounding box aspect ratios among the annotations. Experiments on two additional annotation sets show that the sets with altered box aspect ratios are able to improve the overall defect detection accuracy, particularly for classes containing boxes with very small aspect ratios. We also provide extensive class-wise results with visual examples of the highlighted problem.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
PublisherIEEE
Pages290-295
Number of pages6
ISBN (Electronic)9798350313154
DOIs
Publication statusPublished - 28 Aug 2023
Event2023 IEEE International Conference on Multimedia and Expo Workshops - Brisbane, Australia
Duration: 10 Jul 202314 Jul 2023

Conference

Conference2023 IEEE International Conference on Multimedia and Expo Workshops
Abbreviated titleICMEW 2023
Country/TerritoryAustralia
CityBrisbane
Period10/07/2314/07/23

Keywords

  • Defect detection
  • aspect ratio
  • data annotation
  • deep learning
  • wind turbine blade images

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Control and Optimization

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