  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.
