ASPHALT POTHOLE DETECTION IN UAV IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

Transportation infrastructure needs constant maintenance. Pavement management systems requires reliable and detailed data of the current state of the roads to make effective decisions. Currently, pavement condition evaluation methods are mostly performed manually with visual inspection and interpretations in situ, which is labor intensive, time consuming and expensive. In this paper an experiment was conducted where a set of different configurations and parameters for Convolutional Neural Networks (CNNs) were applied to automatically detect potholes from images taken by an Unmanned Aerial Vehicle (UAV). Results showed that the pre-trained Faster-RCNN Inception ResNet model with reduced anchor box stride and image augmentation applied provides better accuracy compared to several other models tested, obtaining accuracy for this experiment of 70.4% across five-fold cross validation.