Abstract

Conventional methods for monitoring pavement healthy states have the disadvantages of low efficiency and being time-consuming and destructive. Current studies indicate that traditional machine learning algorithms showed poor performance and low generalization capacity in identifying asphalt pavement aging and damage conditions. Further, deep learning network models have less been applied to the detection of asphalt pavement aging types and damage objects from UAV imagery. In this study, we first used a low-altitude UAV platform to acquire multispectral images of road pavement with centimeter-level spatial resolution. The fine spatial resolution can provide detailed textural information of the pavement damage objects such as cracks and potholes. Afterwards, we combined multiscale semantic segmentation, using the CNN model and SVM classifier into a framework to extract pavement potholes and cracks and classify the pavement surfaces into three aging states. Results demonstrated that the proposed framework achieved the highest overall accuracy (87.83% and 92.96%) and recall rate (85.4% and 90.65%) in the classification of the asphalt pavement images in the two segments of roads in Xinjiang, China. We concluded that the combination of the CNN + SVM and low-altitude UAV multispectral images would contribute to improve the accuracy in the detection of asphalt pavement aging states and damaged objects.