ConTrack Distress Dataset: A Continuous Observation for Pavement Deterioration Spatio-Temporal Analysis

Abstract

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.