Automated Pothole Distress Assessment Using Asphalt Pavement Video Data

Potholes, as a severe type of pavement distress, are currently identified and assessed manually in pavement-maintenance programs. This manual process is time-consuming and labor-intensive. Existing methods for automated pothole detection either rely on expensive and high-maintenance range sensors or make use of acceleration data, which only apply when the pothole is on the tires' path. The authors' previous work has proposed and validated a camera-based pothole-detection method. However, this method is limited to single frames and cannot determine the severity of potholes. This paper presents a novel method that addresses these issues by incrementally updating a representative texture template for intact pavement regions and using a vision tracker to reduce the computational effort, improve the detection reliability, and count potholes efficiently. The improved method was implemented and tested on real data. The results indicate a significant capability and performance increase of this method over its predecessor.