Automated Detection and Mapping of Pavement Cracks from Videos for Road Inspections

Abstract. Road inspections are essential for assessing pavement conditions and planning maintenance to extend infrastructure lifespan. Cracks are key indicators of distress, and their timely detection can reduce repair costs. While manual inspections are costly and subjective, automated crack detection using vehicle-mounted cameras and digital image processing has improved efficiency but still requires extensive manual review. Crack detection research typically focuses on improving the accuracy of the detection and feature extraction, neglecting the geospatial mapping part which is essential in pavement inspection. This paper presents an end-to-end pipeline that combines V-SLAM, deep learning (DL) and 2D-3D mapping to detect and map pavement cracks using vehicle-mounted cameras footage and provide georeferenced results interpretable inside a Geospatial Information System (GIS). Results significantly reduce inspection time by providing preliminary detections for rapid verification by expert operators in GIS environments. This enhances data management, efficient validation, advanced spatial analysis and time-based tracking of crack progression, ensuring informed decision-making and optimized maintenance planning, ultimately extending infrastructure lifespan and reducing costs.