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Enhancing tunnel crack detection with linear seam using mixed stride convolution and attention mechanism
Lang Lang1, Xiao-Qin Chen1, Qiang Zhou2
1School of Intelligent Manufacturing, Chongqing Three Gorges Vocational College, Chongqing, 404155, China.
Scientific Reports
|July 1, 2024
Summary
A novel deep learning method enhances tunnel crack detection by improving feature extraction for lining seams. This approach boosts safety and tunnel durability by accurately identifying structural defects.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Cracks in tunnel linings pose significant risks to traffic safety and structural integrity.
- Distinguishing cracks from similar-looking lining seams presents a major detection challenge.
Purpose of the Study:
- To develop an advanced deep learning method for accurate crack detection in tunnel lining structures.
- To improve the identification of subtle cracks that resemble natural lining seams.
Main Methods:
- An improved attention mechanism was developed to capture global and dimensional spatial information for morphological features.
- A mixed strip convolution module was introduced to gather remote contextual information from multiple angles, mitigating background noise.
- These modules were integrated into a U-shaped network architecture.
Main Results:
- The proposed deep learning method demonstrated superior performance compared to existing techniques.
- Experiments on the Tunnel200, Crack500, and DeepCrack datasets validated the effectiveness of the approach.
- The enhanced feature extraction significantly improved crack detection accuracy.
Conclusions:
- The novel deep learning approach effectively addresses the challenge of crack detection in tunnel linings.
- The method offers a promising solution for enhancing tunnel safety and maintenance through accurate defect identification.
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