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

• URL: http://arxiv.org/abs/2604.14711v1
• Date: Thu, 16 Apr 2026 07:21:32 GMT
• Title: MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering
• Abstract summary: This paper proposes MS-SSE-Net, a novel deep learning framework for structural damage classification.<n>The proposed model is built upon the DenseNet201 backbone and integrates novel multi-scale feature extraction.<n> Experiments are conducted on the StructDamage dataset containing multiple structural damage categories.
• Score: 5.485960257223969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Mitigating Shortcut Learning: Texture-Penalized Prototype Networks [0.7816377212023298]
  CNNs exhibit severe performance degradation due to a strong inductive texture bias.<n>To address this flaw, this study introduces the Texture-Penalized Prototype Network (TPPN)<n>TPPN shifts this inherent bias without depending on resource-intensive augmented datasets.
  arXiv  Detail & Related papers  (2026-09-07T13:51:49Z)
• GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering [1.3277276618043652]
  Deep learning-based building footprint extraction often produces top inconsistent vectors for direct GIS database ingestion.<n>We present a multidomain GeoAI quality control framework that automates error detection to purify vector footprint databases.<n>This framework successfully identified 87.34% of erroneous footprints while maintaining 98.31% of acceptable structures.
  arXiv  Detail & Related papers  (2026-08-10T02:55:07Z)
• MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity [65.85858856481131]
  unstructured and irregular nature of point clouds poses a significant challenge for objective quality assessment (PCQA)<n>We propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM)
  arXiv  Detail & Related papers  (2026-01-03T14:58:52Z)
• Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion [20.287188044863925]
  This paper proposes a novel hybrid feature fusion framework for constructing a graph-structured dataset of failure modes.<n>A hierarchical feature fusion framework is constructed, using Word2Vec encoding to encode subsystem/component features, BERT-KPCA to process failure modes/reasons, and Sentence-BERT to quantify the semantic association between failure impact and emergency decision-making.
  arXiv  Detail & Related papers  (2025-07-18T08:02:49Z)
• Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network [4.8748194765816955]
  This study proposes a novel method combining a self-encoder-based data augmentation technique with a convolutional neural network (CNN) The proposed method achieves a classification accuracy of 98.56%, surpassing Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively.
  arXiv  Detail & Related papers  (2024-11-17T10:19:54Z)
• TopoFR: A Closer Look at Topology Alignment on Face Recognition [58.45515807380505]
  We propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE.<n> PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model.<n> Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T14:58:30Z)
• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure [52.2025114590481]
  We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status.
  arXiv  Detail & Related papers  (2024-09-04T16:47:16Z)
• Structural damage detection via hierarchical damage information with volumetric assessment [1.4470320778878742]
  Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure.<n>This study introduces the Guided Detection Network (Guided-DetNet), a framework designed to address these challenges.<n>Guided-DetNet is characterized by a Generative Attention Module (GAM), Hierarchical Elimination Algorithm (HEA), and Volumetric Contour Visual Assessment (VCVA)
  arXiv  Detail & Related papers  (2024-07-29T04:33:04Z)
• Dual flow fusion model for concrete surface crack segmentation [0.0]
  Cracks and other damages pose a significant threat to the safe operation of transportation infrastructure. Deep learning models have been widely applied to practical visual segmentation tasks. This paper proposes a crack segmentation model based on the fusion of dual streams.
  arXiv  Detail & Related papers  (2023-05-09T02:35:58Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)
• ISTR: End-to-End Instance Segmentation with Transformers [147.14073165997846]
  We propose an instance segmentation Transformer, termed ISTR, which is the first end-to-end framework of its kind. ISTR predicts low-dimensional mask embeddings, and matches them with ground truth mask embeddings for the set loss. Benefiting from the proposed end-to-end mechanism, ISTR demonstrates state-of-the-art performance even with approximation-based suboptimal embeddings.
  arXiv  Detail & Related papers  (2021-05-03T06:00:09Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.