A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning

Area of Science:

• Road infrastructure assessment
• Computer vision and machine learning applications
• Traffic safety engineering

Background:

• Road surface damage, such as potholes, presents significant risks to vehicle safety and traffic flow.
• Current manual inspection methods are inefficient, costly, and hazardous.
• Automated systems are needed for accurate and efficient road condition assessment.

Purpose of the Study:

• To provide a comprehensive review of advanced computer vision and machine learning algorithms for pothole detection.
• To analyze sensing systems for acquiring 2D and 3D road data.
• To identify opportunities for future research in automated road assessment.

Main Methods:

• Review of classical 2D image processing algorithms.
• Analysis of segmentation-based algorithms using 3D point cloud modeling.
• Evaluation of machine learning, deep learning, and hybrid approaches for pothole recognition.

Main Results:

• Hybrid methods integrating traditional image processing with machine learning achieve the highest pothole detection accuracy.
• Deep learning approaches show superior adaptability and detection rates.
• Traditional 2D and 3D methods serve as valuable baseline techniques.

Conclusions:

• Vision-based methods, especially hybrid and deep learning approaches, are crucial for advancing automated road surface condition assessment.
• This review clarifies the current technological landscape and guides future development for improved roadway safety and infrastructure management.