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YOLO-ERCD: An Upgraded YOLO Framework for Efficient Road Crack Detection.

Xiao Li^1,2, Ying Chu¹, Thorsten Chan³

¹College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This study introduces YOLO-ERCD, an enhanced AI framework for accurate road damage detection. It improves crack identification and system robustness, crucial for intelligent transportation systems.

Keywords:

Road Crack YOLO deep learning object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Transportation Engineering

Background:

Road damage detection is vital for intelligent transportation systems.
Current models struggle with fine cracks, lighting variations, and complex backgrounds.
Existing visual sensing technologies require enhanced accuracy and robustness.

Purpose of the Study:

To propose YOLO-ERCD, an enhanced YOLO-based framework for improved road crack detection.
To address limitations in feature representation, lighting adaptation, and background interference in existing models.
To enhance accuracy and robustness for automated road inspection using sensor-acquired images.

Main Methods:

Implemented a residual convolutional block attention module for feature representation.
Integrated a channel-wise adaptive gamma correction module for lighting robustness.
Developed a visual focus noise modulation module to reduce background interference.
Utilized datasets from vehicle-mounted and traffic surveillance cameras.

Main Results:

YOLO-ERCD demonstrated superior accuracy and computational efficiency compared to recent models.
The framework effectively addresses challenges in fine crack detection and lighting variations.
Experimental results validated performance on both proprietary and public datasets.

Conclusions:

YOLO-ERCD offers a robust and efficient solution for road damage detection.
The lightweight design enables real-time deployment on edge devices.
The study highlights the potential of AI-based visual sensing for advanced road monitoring.