Multi-Sensor Attention Networks for Automated Subsurface Delamination Detection in Concrete Bridge Decks
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910013832822784 |
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| author | Moayedikia, Alireza Moayedikia, Amirhossein |
| author_facet | Moayedikia, Alireza Moayedikia, Amirhossein |
| contents | Subsurface delaminations in concrete bridge decks remain undetectable through conventional visual inspection, necessitating automated non-destructive evaluation methods. This work introduces a deep learning framework that integrates Ground Penetrating Radar (GPR) and Infrared Thermography (IRT) through hierarchical attention mechanisms. Our architecture employs temporal self-attention to process GPR electromagnetic signals, spatial attention to analyze thermal imagery, and cross-modal attention with learnable embeddings to model inter-sensor correspondences. We integrate Monte Carlo dropout-based uncertainty quantification, decomposing prediction confidence into model uncertainty and data-driven uncertainty components. Testing across five real-world bridge datasets from the SDNET2021 benchmark reveals that our approach delivers substantial performance gains over single-sensor and concatenation-based baselines when applied to balanced or moderately imbalanced data distributions. Comprehensive ablation analysis confirms that cross-modal attention mechanisms contribute meaningful improvements beyond unimodal attention alone. Critically, we identify and characterize specific failure modes: under extreme class imbalance, attention-based architectures demonstrate susceptibility to majority class bias, indicating scenarios where simpler architectural choices may prove more robust. Our findings equip practitioners with empirically-grounded criteria for selecting appropriate fusion strategies based on dataset characteristics, rather than promoting universal architectural superiority. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Sensor Attention Networks for Automated Subsurface Delamination Detection in Concrete Bridge Decks Moayedikia, Alireza Moayedikia, Amirhossein Computer Vision and Pattern Recognition Image and Video Processing Subsurface delaminations in concrete bridge decks remain undetectable through conventional visual inspection, necessitating automated non-destructive evaluation methods. This work introduces a deep learning framework that integrates Ground Penetrating Radar (GPR) and Infrared Thermography (IRT) through hierarchical attention mechanisms. Our architecture employs temporal self-attention to process GPR electromagnetic signals, spatial attention to analyze thermal imagery, and cross-modal attention with learnable embeddings to model inter-sensor correspondences. We integrate Monte Carlo dropout-based uncertainty quantification, decomposing prediction confidence into model uncertainty and data-driven uncertainty components. Testing across five real-world bridge datasets from the SDNET2021 benchmark reveals that our approach delivers substantial performance gains over single-sensor and concatenation-based baselines when applied to balanced or moderately imbalanced data distributions. Comprehensive ablation analysis confirms that cross-modal attention mechanisms contribute meaningful improvements beyond unimodal attention alone. Critically, we identify and characterize specific failure modes: under extreme class imbalance, attention-based architectures demonstrate susceptibility to majority class bias, indicating scenarios where simpler architectural choices may prove more robust. Our findings equip practitioners with empirically-grounded criteria for selecting appropriate fusion strategies based on dataset characteristics, rather than promoting universal architectural superiority. |
| title | Multi-Sensor Attention Networks for Automated Subsurface Delamination Detection in Concrete Bridge Decks |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2512.20113 |