  Wind turbines are subjected to continuous rotational stresses and unusual
external forces such as storms, lightning, strikes by flying objects, etc.,
which may cause defects in turbine blades. Hence, it requires a periodical
inspection to ensure proper functionality and avoid catastrophic failure. The
task of inspection is challenging due to the remote location and inconvenient
reachability by human inspection. Researchers used images with cropped defects
from the wind turbine in the literature. They neglected possible background
biases, which may hinder real-time and autonomous defect detection using aerial
vehicles such as drones or others. To overcome such challenges, in this paper,
we experiment with defect detection accuracy by having the defects with the
background using a two-step deep-learning methodology. In the first step, we
develop virtual models of wind turbines to synthesize the near-reality images
for four types of common defects - cracks, leading edge erosion, bending, and
light striking damage. The Unity perception package is used to generate wind
turbine blade defects images with variations in background, randomness, camera
angle, and light effects. In the second step, a customized U-Net architecture
is trained to classify and segment the defect in turbine blades. The outcomes
of U-Net architecture have been thoroughly tested and compared with 5-fold
validation datasets. The proposed methodology provides reasonable defect
detection accuracy, making it suitable for autonomous and remote inspection
through aerial vehicles.
