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This paper introduces MS-SSE-Net, a deep learning framework for structural damage classification, built upon DenseNet201. The key innovation is the integration of multi-scale feature extraction using parallel depthwise convolutions with squeeze-and-excitation style channel and spatial attention mechanisms to refine features. Experiments on the StructDamage dataset demonstrate that MS-SSE-Net achieves superior performance, reaching 99.26% accuracy, outperforming the DenseNet201 baseline.
Channel and spatial attention, when combined with multi-scale feature extraction, can push structural damage classification accuracy to near-perfect levels.
Structural damage detection is essential for maintaining the safety and reliability of civil infrastructure. However, accurately identifying different types of structural damage from images remains challenging due to variations in damage patterns and environmental conditions. To address these challenges, this paper proposes MS-SSE-Net, a novel deep learning (DL) framework for structural damage classification. The proposed model is built upon the DenseNet201 backbone and integrates novel multi-scale feature extraction with channel and spatial attention mechanisms (MS-SSE-Net). Specifically, parallel depthwise convolutions capture both local and contextual features, while squeeze-and-excitation style channel attention and spatial attention emphasize informative regions and suppress irrelevant noise. The refined features are then processed through global average pooling and a fully connected classification layer to generate the final predictions. Experiments are conducted on the StructDamage dataset containing multiple structural damage categories. The proposed MS-SSE-Net demonstrates superior performance compared with the baseline DenseNet201 and other comparative approaches. Specifically, the proposed method achieves 99.31% precision, 99.25% recall, 99.27% F1-score, and 99.26% accuracy, outperforming the baseline model which achieved 98.62% precision, 98.53% recall, 98.58% F1-score, and 98.53% accuracy.
