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Summary

This study introduces a robust computer vision algorithm for accurate pothole detection in road maintenance. The new method achieves high detection accuracy, improving road safety and infrastructure management.

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Area of Science:

Computer Vision
Road Maintenance Engineering

Background:

Pothole detection is crucial for road maintenance.
Current computer vision methods for pothole detection are often used independently (2D or 3D) and lack satisfactory accuracy.

Purpose of the Study:

To develop a robust, accurate, and computationally efficient pothole detection algorithm.
To integrate 2D and 3D road analysis for improved pothole detection.

Main Methods:

Dense disparity map transformation using golden section search and dynamic programming.
Otsu's thresholding for extracting undamaged road areas.
Quadratic surface modeling with surface normals and random sample consensus (RANSAC) for robust disparity map analysis.

Main Results:

The proposed algorithm achieves a successful detection accuracy of approximately 98.7%.
Overall pixel-level accuracy reaches around 99.6%.
Point clouds of detected potholes are extracted from the reconstructed 3D road surface.

Conclusions:

The developed algorithm significantly improves pothole detection accuracy and efficiency.
Integrating 3D road surface modeling with advanced computer vision techniques enhances pothole identification.
The method offers a promising solution for automated road maintenance and safety.