  Pavement conditions are a critical aspect of asset management and directly
affect safety. This study introduces a deep neural network method called U-Net
for pavement crack segmentation based on drone-captured images to reduce the
cost and time needed for airport runway inspection. The proposed approach can
also be used for highway pavement conditions assessment during off-peak periods
when there are few vehicles on the road. In this study, runway pavement images
are collected using drone at various heights from the Fitchburg Municipal
Airport (FMA) in Massachusetts to evaluate their quality and applicability for
crack segmentation, from which an optimal height is determined. Drone images
captured at the optimal height are then used to evaluate the crack segmentation
performance of the U-Net model. Deep learning methods typically require a huge
set of annotated training datasets for model development, which can be a major
obstacle for their applications. An online annotated pavement image dataset is
used together with the FMA data to train the U-Net model. The results show that
U-Net performs well on the FMA testing data even with limited FMA training
images, suggesting that it has good generalization ability and great potential
to be used for both airport runways and highway pavements.
