Road cavity detection from GPR data using deep learning
Abstract
This repository contains the supplementary data, source code, and trained models for the research paper: Elseicy, A., Arias, P., & Solla, M. (2026). Automatic identification of pavement subsurface distresses from ground penetrating radar data using few-shot data augmentation and YOLOv11. Journal of Road Engineering, 6(3), 492–506. https://doi.org/10.1016/j.jreng.2026.01.004
Fields of Study & Subjects
Infrastructure Maintenance and Monitoring
Geophysical Methods and Applications
Geotechnical and construction materials studies
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 19 January 2026