# Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework

arXiv: https://arxiv.org/abs/2108.08636 · HTML: https://mlsanity.com/paper/2108.08636/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)

## Built on by (most cited first, 1 of 1)

- [Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images](https://mlsanity.com/paper/2207.11186/) (2022)

## Abstract (arXiv)

In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The surface damage detection of wind turbine blade requires a large dataset so as to detect a type of damage at an early stage. Turbine blade images are captured via aerial imagery. Upon inspection, it is found that the image dataset was limited and hence image augmentation is applied to improve blade image dataset. The approach is modeled as a multi-class supervised learning problem and deep learning methods like Convolutional neural network (CNN), VGG16-RCNN and AlexNet are tested for determining the potential capability of turbine blade surface damage.