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Pavement conditions are a critical aspect of asset management and directlyaffect safety. This study introduces a deep neural network method called U-Netfor pavement crack segmentation based on drone-captured images to reduce thecost and time needed for airport runway inspection. The proposed approach canalso be used for highway pavement conditions assessment during off-peak periodswhen there are few vehicles on the road. In this study, runway pavement imagesare collected using drone at various heights from the Fitchburg MunicipalAirport (FMA) in Massachusetts to evaluate their quality and applicability forcrack segmentation, from which an optimal height is determined. Drone imagescaptured at the optimal height are then used to evaluate the crack segmentationperformance of the U-Net model. Deep learning methods typically require a hugeset of annotated training datasets for model development, which can be a majorobstacle for their applications. An online annotated pavement image dataset isused together with the FMA data to train the U-Net model. The results show thatU-Net performs well on the FMA testing data even with limited FMA trainingimages, suggesting that it has good generalization ability and great potentialto be used for both airport runways and highway pavements.

A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images