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Road damage detection algorithm for improved YOLOv5.
Gege Guo1, Zhenyu Zhang2,3
1College of Software, Xinjiang University, Urumqi, 830046, China.
Scientific Reports
|September 15, 2022
Summary
This study introduces MN-YOLOv5, an improved algorithm for road damage detection. It enhances accuracy and efficiency in identifying pavement cracks, offering a cost-effective solution for road maintenance.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Road Engineering
Background:
- Manual road damage detection is inefficient and costly.
- Timely repair of road damage is crucial for safety.
- Existing automated methods require optimization.
Purpose of the Study:
- To develop an efficient and accurate road damage detection algorithm.
- To improve upon the YOLOv5 model for pavement crack identification.
- To reduce model size and computational cost.
Main Methods:
- Optimized YOLOv5s with MobileNetV3 backbone.
- Integrated coordinate attention and KMeans clustering.
- Applied label smoothing and structure reparameterization.
Main Results:
- MN-YOLOv5 demonstrated effective pavement crack identification.
- Achieved a 2.5% increase in mAP and 2.6% in F1 score.
- Reduced model size by 1.62x, parameters by 1.66x, and GFLOPs by 1.69x.
Conclusions:
- The proposed MN-YOLOv5 algorithm offers superior performance and efficiency.
- This method provides a viable reference for automated pavement crack detection.
- The optimized model contributes to enhanced road safety and maintenance.
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