Estimation of road surface quality using real-time video analysis

Abstract

An accurate and consistent survey of road surface distresses is critical for pavement rehabilitation design and management, allowing public managers to maximize the value of constantly limited budgets for road improvements and maintenance. Manual pavement distress surveys are time-consuming, costly, and dangerous on heavily traveled highways. Automated surveys using video recording hardware devices have been developed and improved over the years, to solve the problems associated with manual surveys. However, reliable distress detection software and data analysis remain difficult. With the advances in smartphone technology, it is now possible to use mounted devices in the field effectively for such applications. A smartphone application was previously developed to utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. This study examines the possibility of using real-time video processing for pavement surface quality detection. Video captured from the mounted camera is analyzed to estimate road conditions and correlated with sensor data ground truth to corroborate the efficacy of the technique. The findings of this study could indeed aid in developing more effective uses of specialized software for pavement condition classification, to assist decision makers in selecting solutions based on budget and desired survey accuracy, and to evaluate how existing devices will perform when used with the developed algorithm.