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

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.