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YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance.

Wei Wang¹, Xiaoru Yu¹, Bin Jing¹

¹College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, China.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This study introduces YOLO-RD, a new framework for road damage detection. It significantly improves the accuracy of identifying small road defects, enhancing road safety and maintenance efficiency.

Keywords:

YOLO-RD multi-dimensional auxiliary fusion road damage detection star operation module wavelet transform convolution

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Area of Science:

Computer Vision
Artificial Intelligence
Road Engineering

Background:

Road damage detection is vital for safety and cost-effective maintenance.
Current methods struggle with small, irregular damages and complex backgrounds.
Automated road condition monitoring requires robust detection systems.

Purpose of the Study:

To develop an advanced road damage detection framework (YOLO-RD).
To enhance detection accuracy for small-scale and irregularly shaped road damages.
To improve robustness in complex environmental conditions for road monitoring.

Main Methods:

Proposed YOLO-RD framework integrating Star Operation Module (SOM), Multi-dimensional Auxiliary Fusion (MAF), and Wavelet Transform Convolution (WTC).
SOM enhances sensitivity to small-scale damage.
MAF improves robustness in complex backgrounds, and WTC focuses on irregular shapes.

Main Results:

YOLO-RD achieved 25.75% detection accuracy on the RDD2022 dataset.
Demonstrated a 4.93% improvement in small object detection compared to YOLOv8.
Effectively addressed challenges of small damage, complex backgrounds, and irregular shapes.

Conclusions:

YOLO-RD offers a practical and effective solution for automated road condition monitoring.
The integrated modules significantly enhance detection capabilities for diverse road damage scenarios.
The framework shows strong potential for improving road safety and maintenance strategies.