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Concrete Surface Crack Recognition Based on Coordinate Attention Neural Networks
1School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China.
Computational Intelligence and Neuroscience
|August 22, 2022
Summary
This study introduces CaNet, a deep learning model for concrete crack identification in transportation infrastructure. CaNet enhances crack detection accuracy, improving infrastructure maintenance and safety.
Area of Science:
- Civil Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Concrete cracks are a prevalent issue in highway and tunnel infrastructure, compromising structural integrity and safety.
- Effective identification and reporting of concrete cracks are crucial for infrastructure maintenance, preventing economic losses and ensuring public safety.
Purpose of the Study:
- To propose a novel deep learning network, CaNet, for accurate and efficient identification of concrete cracks.
- To enhance the detection capabilities for narrow crack areas within concrete structures.
Main Methods:
- Developed CaNet, a deep learning network utilizing ResNet50 as its backbone.
- Integrated coordinate attention mechanism into ResNet50's residual units to improve capture of cross-channel, direction-aware, and position-sensitive information.
- Focused on enhancing the network's ability to locate small crack regions.
Main Results:
- CaNet achieved an accuracy rate of 89.6% in experiments, outperforming compared networks.
- The model demonstrated strong performance with a recall of 86%, F1 score of 85%, and precision of 87%.
Conclusions:
- The CaNet model is highly effective for identifying concrete cracks in transportation infrastructure.
- The integration of coordinate attention significantly improves the network's precision in locating narrow crack areas.
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