Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios
This work addresses the need for cost-effective and efficient airport pavement inspection for airport operators, though it is incremental as it builds on existing deep learning methods with dataset augmentation.
The authors tackled the problem of automatically identifying airport pavement distress using UAV-captured images by proposing a vision-based deep learning approach that segments defects, and they demonstrated that training with a mixed dataset of synthetic and real images improves results in real scenarios.
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.