  Runway and taxiway pavements are exposed to high stress during their
projected lifetime, which inevitably leads to a decrease in their condition
over time. To make sure airport pavement condition ensure uninterrupted and
resilient operations, it is of utmost importance to monitor their condition and
conduct regular inspections. UAV-based inspection is recently gaining
importance due to its wide range monitoring capabilities and reduced cost. In
this work, we propose a vision-based approach to automatically identify
pavement distress using images captured by UAVs. The proposed method is based
on Deep Learning (DL) to segment defects in the image. The DL architecture
leverages the low computational capacities of embedded systems in UAVs by using
an optimised implementation of EfficientNet feature extraction and Feature
Pyramid Network segmentation. To deal with the lack of annotated data for
training we have developed a synthetic dataset generation methodology to extend
available distress datasets. We demonstrate that the use of a mixed dataset
composed of synthetic and real training images yields better results when
testing the training models in real application scenarios.

Computer Vision and Pattern Recognition