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A pavement distresses identification method optimized for YOLOv5s.

Keyou Guo¹, Chengbo He², Min Yang¹

¹Beijing Technology and Business University, Beijing, 100037, China.

Scientific Reports

|March 4, 2022

Summary

This summary is machine-generated.

This study introduces an improved YOLOv5 model for automatic pavement distress detection, enhancing road safety and maintenance. The optimized model achieves high accuracy, offering a technical reference for pavement distress detection robots.

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Area of Science:

Computer Vision
Artificial Intelligence
Civil Engineering

Background:

Timely pavement repair is crucial for preventing road structure destruction and traffic accidents.
Detecting pavement distresses is challenging due to factors like single object categories, shading, and occlusion.

Purpose of the Study:

To develop an improved YOLOv5 model for accurate and robust automatic detection of various pavement distresses.
To enhance the model's suitability for deployment on embedded devices for intelligent mobile platforms.

Main Methods:

Optimization of the YOLOv5 model architecture.
Integration of an attention mechanism to improve model robustness.
Deployment of the optimized model on a self-built intelligent mobile platform.

Main Results:

The improved YOLOv5 model achieved high performance metrics: 95.5% precision, 94.3% recall, and 95% mAP (mean Average Precision).
The model demonstrated superior performance compared to YOLOv5s and YOLOv4, with mAP increases of 4.3% and 25.8%, respectively.
Effective identification of pavement distresses was confirmed on the intelligent mobile platform and custom datasets.

Conclusions:

The proposed improved YOLOv5 network model effectively detects pavement distresses using an intelligent mobile platform.
This method provides a valuable technical reference for the development of pavement distress detection robots.
The enhanced model's robustness and accuracy contribute to improved road maintenance strategies.