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GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments
Yuhao Wang1,2, Heran Zhu3, Yirong Wang1,2
1College of Water Conservancy and Hydropower, Sichuan Agricultural University, Yaan, Sichuan, China.
Scientific Reports
|July 22, 2025
Summary
This study introduces an enhanced GSB-YOLO model for efficient road crack detection. The improved model enhances detection accuracy and robustness in complex environments, crucial for road safety.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Road crack detection is vital for traffic safety and infrastructure maintenance.
- Existing methods struggle with varying crack scales, large model sizes, and complex backgrounds.
Purpose of the Study:
- To propose an enhanced GSB-YOLO model for improved road crack detection.
- To address limitations of current methods, including efficiency and robustness.
Main Methods:
- Developed a lightweight network structure using linear transformation and long-range attention.
- Introduced a novel SMC2f module for dynamic neuron weighting in the neck structure.
- Optimized Path Aggregation Network (PAN) and Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature fusion.
Main Results:
- The enhanced GSB-YOLO model demonstrated improved detection efficiency and robustness.
- Achieved a 3.2% increase in mean average precision (mAP) for road crack detection.
- Successfully addressed challenges of varying target scales and complex backgrounds.
Conclusions:
- The proposed GSB-YOLO model offers significant advancements in road crack detection.
- The model shows substantial application value for ensuring road and traffic safety.
- This research contributes to more reliable infrastructure monitoring systems.
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