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.

翻译：随着风电行业的持续增长，设计用于风电机组维护的自动化数据驱动解决方案变得尤为迫切。由于结构健康监测主要依赖于视觉检查，任何自动化解决方案的首要环节均为在图像中识别叶片区域。为此，我们提出一种新型分割算法，通过定制化损失函数增强U-Net性能——该损失函数将焦点损失与连续性正则化项相结合。为实现顶尖性能，我们设计了一系列附加步骤以确保算法的可靠性、泛化性、鲁棒性和高效性：首先，利用对图像的先验知识，通过填充由临时分类的叶片像素与图像边界围成的空洞区域来修正分割结果；随后，通过实时训练的随机森林成功修正误分类像素。实验表明，该算法效能显著，达到了97.39%的高精度。