  Dents on the aircraft skin are frequent and may easily go undetected during
airworthiness checks, as their inspection process is tedious and extremely
subject to human factors and environmental conditions. Nowadays, 3D scanning
technologies are being proposed for more reliable, human-independent
measurements, yet the process of inspection and reporting remains laborious and
time consuming because data acquisition and validation are still carried out by
the engineer. For full automation of dent inspection, the acquired point cloud
data must be analysed via a reliable segmentation algorithm, releasing humans
from the search and evaluation of damage. This paper reports on two
developments towards automated dent inspection. The first is a method to
generate a synthetic dataset of dented surfaces to train a fully convolutional
neural network. The training of machine learning algorithms needs a substantial
volume of dent data, which is not readily available. Dents are thus simulated
in random positions and shapes, within criteria and definitions of a Boeing 737
structural repair manual. The noise distribution from the scanning apparatus is
then added to reflect the complete process of 3D point acquisition on the
training. The second proposition is a surface fitting strategy to convert 3D
point clouds to 2.5D. This allows higher resolution point clouds to be
processed with a small amount of memory compared with state-of-the-art methods
involving 3D sampling approaches. Simulations with available ground truth data
show that the proposed technique reaches an intersection-over-union of over
80%. Experiments over dent samples prove an effective detection of dents with a
speed of over 500 000 points per second.
