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.

翻译：跑道和滑行道道面在其设计服役期内承受高应力作用，不可避免地导致其状况随时间推移而下降。为确保机场道面状态保障运行连续性与韧性，对其状况进行监测并实施定期检查至关重要。基于无人机的检测因其广域监测能力与低成本优势，近年来日益受到重视。本研究提出一种基于视觉的自动识别方法，利用无人机采集的图像自动识别道面病害。该方法基于深度学习对图像中的缺陷进行分割。深度学习架构通过采用轻量化EfficientNet特征提取网络与特征金字塔分割网络的优化部署方案，适配无人机嵌入式系统的低算力约束。针对训练数据标注不足的问题，我们开发了一套合成数据集生成方法以扩充现有病害数据集。实验结果表明，在真实应用场景下，采用合成图像与真实训练图像构成的混合数据集进行模型训练，可获得更优的检测效果。