Smartphone-based accurate pothole depth estimation using monocular RGB and LiDAR-guided deep learning.

The maintenance of the road infrastructure is vital to ensure public safety and the efficiency of transportation, but the unpredictable and widespread occurrence of potholes often hinders continuous monitoring. Traditional detection approaches typically rely on expensive equipment or manual inspections, rendering them impractical for large-scale, real-time deployment. To overcome these limitations, we present a low-cost and scalable pothole assessment framework that leverages the built-in LiDAR sensors available in modern smartphones in combination with a hybrid deep learning pipeline. The proposed system fuses RGB imagery with LiDAR-guided refinement to achieve precise depth estimation and accurate pothole segmentation, further enhanced through post-processing for improved boundary alignment. A comprehensive dataset of 25,000 RGB-LiDAR image pairs, collected under various road and lighting conditions, was curated to train and evaluate the framework. Extensive experiments demonstrate that our approach achieves high accuracy in both depth estimation and pothole delineation, significantly outperforming existing methods. This work highlights the potential of lightweight smartphone-based solutions for real-time automated road condition assessment, offering a practical and economical tool for modern infrastructure management.