# Robust Wind Turbine Blade Segmentation from RGB Images in the Wild

arXiv: https://arxiv.org/abs/2306.14810 · HTML: https://mlsanity.com/paper/2306.14810/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Disentangled Non-Local Neural Networks](https://mlsanity.com/paper/2006.06668/) (2020)
- [Switchable Whitening for Deep Representation Learning](https://mlsanity.com/paper/1904.09739/) (2019)
- [Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2206.09293/) (2022)
- [UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation](https://mlsanity.com/paper/2204.00631/) (2022)

## Built on by (most cited first, 8 of 8)

- [Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios](https://mlsanity.com/paper/2406.06165/) (2024)
- [Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation](https://mlsanity.com/paper/2412.20838/) (2024)
- [Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation](https://mlsanity.com/paper/2601.04065/) (2026)
- [Discriminant Learning-based Colorspace for Blade Segmentation](https://mlsanity.com/paper/2601.13816/) (2026)
- [Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation](https://mlsanity.com/paper/2601.13852/) (2026)
- [Meteorological data and Sky Images meets Neural Models for Photovoltaic Power Forecasting](https://mlsanity.com/paper/2602.15782/) (2026)
- [End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines](https://mlsanity.com/paper/2603.29927/) (2026)
- [Deep Image Segmentation via Discriminant Feature Learning](https://mlsanity.com/paper/2605.14609/) (2026)

## Abstract (arXiv)

With the relentless growth of the wind industry, there is an imperious need to design automatic data-driven solutions for wind turbine maintenance. As structural health monitoring mainly relies on visual inspections, the first stage in any automatic solution is to identify the blade region on the image. Thus, we propose a novel segmentation algorithm that strengthens the U-Net results by a tailored loss, which pools the focal loss with a contiguity regularization term. To attain top performing results, a set of additional steps are proposed to ensure a reliable, generic, robust and efficient algorithm. First, we leverage our prior knowledge on the images by filling the holes enclosed by temporarily-classified blade pixels and by the image boundaries. Subsequently, the mislead classified pixels are successfully amended by training an on-the-fly random forest. Our algorithm demonstrates its effectiveness reaching a non-trivial 97.39% of accuracy.