Method for detecting road pavement damage based on deep learning

Abstract

A safe and healthy road condition plays a supporting role in the public travel and the national economy. Therefore, effective management and maintenance methods have become the key problems that the researchers and engineers are urgently solving, early damage detection and warning are also important for disaster emergency treatment, but some traditional road damage identification methods are often costly and need to be equipped with professional persons. Due to the complexity of pavement conditions, some existing defects datasets are not perfect, although the accuracy is high, they cannot be put into practical use. Based on the object detection technology of deep learning, the author introduced a novel method which is more effective and relatively cheap. In this paper, 5966 images with road damage of different angles and distances were collected, and the damage categories included Lateral Crack, Longitudinal Crack, Pothole and separation, Alligator Crack, and Damage around the well cover which had never been considered in the datasets in any researches. After training with GPU using convolutional neural network, the average precision can reach 96.3%.