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

Waqar Rauf Butt¹, Muhammad Farooq¹, Sohail Jabbar²

¹Department of Information Technology, University of the Punjab, Lahore, Pakistan.

|May 8, 2026

Summary

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This summary is machine-generated.

This study introduces a low-cost smartphone framework using LiDAR for accurate pothole detection. It enables efficient, real-time road condition assessment for infrastructure management.

Area of Science:

Civil Engineering
Computer Science
Geospatial Technology

Background:

Road infrastructure maintenance is crucial for safety and transportation efficiency.
Potholes pose a significant challenge to continuous road monitoring.
Existing detection methods are often costly and impractical for large-scale, real-time use.

Purpose of the Study:

To develop a low-cost, scalable framework for pothole assessment.
To leverage smartphone LiDAR sensors for automated road condition monitoring.
To improve the accuracy and efficiency of pothole detection and delineation.

Main Methods:

Utilized a hybrid deep learning pipeline fusing RGB imagery with LiDAR data.
Implemented LiDAR-guided refinement for precise depth estimation and segmentation.

Related Experiment Videos

Developed a post-processing step for enhanced boundary alignment.

Curated a dataset of 25,000 RGB-LiDAR image pairs for training and validation.

Main Results:

Achieved high accuracy in depth estimation and pothole segmentation.
Demonstrated superior performance compared to existing pothole detection methods.
Validated the framework's effectiveness across diverse road and lighting conditions.

Conclusions:

Smartphone-based LiDAR offers a practical and economical solution for real-time road assessment.
The proposed framework provides a valuable tool for modern infrastructure management.
Enables efficient and automated monitoring of road conditions to enhance public safety.