Smartphone-based accurate pothole depth estimation using monocular RGB and LiDAR-guided deep learning
Clinical Snapshot
PICO Framework
| P — Population | Road infrastructure/potholes under various road and lighting conditions |
| I — Intervention | Smartphone-based LiDAR and RGB imaging with hybrid deep learning pipeline |
| C — Comparator | Existing pothole detection methods (traditional equipment/manual inspection) |
| O — Outcomes | Depth estimation accuracy, pothole segmentation precision, boundary alignment |
Bottom Line
This technology validation study presents a promising smartphone-based solution for automated pothole detection using LiDAR sensors and deep learning. While the approach appears innovative and cost-effective, the abstract lacks crucial performance metrics and methodological details needed for proper evaluation. The large dataset (25,000 image pairs) and multi-modal approach are strengths, but without specific accuracy figures, statistical analysis, or validation protocols, the clinical utility remains uncertain. The technology could potentially revolutionise road infrastructure monitoring in Australia, offering a scalable alternative to expensive traditional methods. However, practitioners should await full publication with detailed performance data before considering implementation. The study represents early-stage technology development rather than validated clinical evidence.
Key Findings
P Value: Not reported
Effect Size: Not specified in abstract
Primary Outcome: High accuracy in depth estimation and pothole delineation
Nnt Or Sensitivity: Claims to 'significantly outperform existing methods' but no specific metrics provided
Confidence Interval: Not reported
Clinical Application
High - leverages existing smartphone technology, cost-effective implementation Highly relevant for Australian road maintenance programs, could integrate with state transport authority monitoring systems, potential for citizen reporting initiatives Road infrastructure management authorities, transport departments, local councils
Abstract
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.
References
- 1.Butt, R., Waqar, Farooq, M., Jabbar, S., Iqbal, M. M., Raza, U., & Ahmed, A. E. S. (2026). Smartphone-based accurate pothole depth estimation using monocular RGB and LiDAR-guided deep learning. PLoS One. https://doi.org/10.1371/journal.pone.0344604
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