Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor

Abstract

Current pavement condition–assessment procedures are extensively time consuming and laborious; in addition, these approaches pose safety threats to the personnel involved in the process. In this study, a RGB-D sensor is used to detect and quantify defects in pavements. This sensor system consists of a RGB color image, and an infrared projector and a camera that act as a depth sensor. An approach, which does not need any training, is proposed to interpret the data sensed by this inexpensive sensor. This system has the potential to be used for autonomous cost-effective assessment of road-surface conditions. Various road conditions including patching, cracks, and potholes are autonomously detected and, most importantly, quantified, using the proposed approach. Several field experiments have been carried out to evaluate the capabilities, as well as the limitations of the proposed system. The global positioning system information is incorporated with the proposed system to localize the detected defects. This approach has the potential to be deployed as a supplementary sensor system in pavement surface–assessment vehicles and reduce the operation cost.