Digital analytics and analysis of pavement networks

Abstract

This utility model relates to a modular pavement management system for automated pavement distress detection, quantification, and geospatial visualization using synchronized depth imagery, RGB imagery, and GPS-derived geolocation data mounted on a surveying vehicle. The system comprises a removable custom mounting assembly supporting depth cameras for pavement surface profiling, environmental cameras for contextual imaging, GPS module for logging coordinates and vehicle speed, a memory unit, and a central processing unit executing machine learning algorithms. Acquired multi sensor data are synchronized, pre-processed, and stitched into continuous pavement strip images of predefined length, nominally ten meters, with frame density dynamically adjusted based on real-time vehicle speed. Neural-network-based detection and segmentation models identify and classify pavement distresses including cracks, rutting, potholes, patching, manholes, and surface roughness, generating pixel-level outputs with geotagged attributes. The system provides an objective, efficient, and cost-effective alternative to manual inspections, improving consistency, spatial accuracy, and scalability of pavement condition assessment.