  Wind energy is expected to be one of the leading ways to achieve the goals of
the Paris Agreement but it in turn heavily depends on effective management of
its operations and maintenance (O&M) costs. Blade failures account for
one-third of all O&M costs thus making accurate detection of blade damages,
especially cracks, very important for sustained operations and cost savings.
Traditionally, damage inspection has been a completely manual process thus
making it subjective, error-prone, and time-consuming. Hence in this work, we
bring more objectivity, scalability, and repeatability in our damage inspection
process, using deep learning, to miss fewer cracks. We build a deep learning
model trained on a large dataset of blade damages, collected by our drone-based
inspection, to correctly detect cracks. Our model is already in production and
has processed more than a million damages with a recall of 0.96. We also focus
on model interpretability using class activation maps to get a peek into the
model workings. The model not only performs as good as human experts but also
better in certain tricky cases. Thus, in this work, we aim to increase wind
energy adoption by decreasing one of its major hurdles - the O\&M costs
resulting from missing blade failures like cracks.
