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Research on Road Internal Disease Identification Algorithm Based on Attention Fusion Mechanisms
Yangyang Wang1,2, Shoujing Yan2,3, Chenchen Xi2,3
1Department of Architecture and Civil Engineering, Zhejiang University, Hangzhou 310030, China.
Sensors (Basel, Switzerland)
|October 26, 2024
Summary
This study introduces a deep learning model for identifying internal asphalt pavement diseases. The attention-based multi-view algorithm significantly improves disease detection efficiency and accuracy.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Internal diseases in asphalt pavement are critical indicators of pavement health.
- Accurate identification is vital for effective maintenance and fund allocation in highway management.
Purpose of the Study:
- To develop and evaluate a deep learning algorithm for efficient and accurate identification of internal pavement diseases.
- To improve the overall quality and management of asphalt pavements.
Main Methods:
- A multi-view recognition algorithm model based on deep learning was proposed.
- Attention fusion mechanisms were embedded between channels and between views within the model.
- Performance was evaluated by comparing different neural network models.
Main Results:
- The proposed multi-view recognition algorithm model with attention fusion demonstrated superior performance.
- This model achieved the best results in identifying internal pavement diseases compared to other tested networks.
Conclusions:
- Deep learning, particularly with attention fusion mechanisms, offers a powerful approach for internal pavement disease identification.
- The developed model enhances the efficiency and accuracy of pavement health assessment, aiding maintenance decisions.
