Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality. The collected images were processed and annotated for model training. Three state-of-the-art object-detection algorithms—Faster R-CNN, YOLOv3, and YOLOv4, were used to train the dataset, and their prediction performances were compared. A pavement image dataset was established with six types of distress. YOLOv3 demonstrated the best performance of the three algorithms, with a mean average precision (MAP) of 56.6%. The findings of this study assist in the inspection of non-destructive automatic pavement conditions.

路面破损检测对于维护计划的决策至关重要。无人机 (UAV) 有助于收集路面图像。本文提出了使用带有高分辨率相机的无人机收集路面遇险信息。组装了一个用于路面图像采集的无人机平台，并研究了飞行设置以获得最佳图像质量。对收集的图像进行处理和注释以进行模型训练。三种最先进的对象检测算法——Faster R-CNN、YOLOv3 和 YOLOv4，用于训练数据集，并比较了它们的预测性能。建立了包含六种遇险类型的路面图像数据集。YOLOv3 展示了三种算法的最佳性能，平均精度（MAP）为 56.6%。