MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering

arXiv cs.CV / 4/17/2026

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Key Points

The paper introduces MS-SSE-Net, a deep learning model for classifying structural damage in civil and geotechnical images despite variations in damage patterns and environmental conditions.
MS-SSE-Net builds on a DenseNet201 backbone while adding multi-scale feature extraction using parallel depthwise convolutions plus channel attention (squeeze-and-excitation) and spatial attention to focus on informative regions.
The network refines learned features through global average pooling followed by a fully connected layer to produce final damage predictions.
Experiments on the StructDamage dataset covering multiple damage categories show notably higher performance than the DenseNet201 baseline and other comparison methods, with reported metrics around 99.25–99.31%.
Overall results indicate that the combination of multi-scale representations and attention mechanisms improves both precision/recall balance and classification accuracy for structural damage detection.

Abstract

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.