AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models
Article
Aydin, Mehmet, Barua, Prabal Datta, Chadalavada, Sreenivasulu, Dogan, Sengul, Tuncer, Turker, Chakraborty, Subrata and Acharya, Rajendra U.. 2025. "AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models." Multimedia Tools and Applications. 84 (4), pp. 1821-1843. https://doi.org/10.1007/s11042-024-19163-2
| Article Title | AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models |
|---|---|
| ERA Journal ID | 18083 |
| Article Category | Article |
| Authors | Aydin, Mehmet, Barua, Prabal Datta, Chadalavada, Sreenivasulu, Dogan, Sengul, Tuncer, Turker, Chakraborty, Subrata and Acharya, Rajendra U. |
| Journal Title | Multimedia Tools and Applications |
| Journal Citation | 84 (4), pp. 1821-1843 |
| Number of Pages | 23 |
| Year | 2025 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 1380-7501 |
| 1573-7721 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11042-024-19163-2 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11042-024-19163-2 |
| Abstract | In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024. |
| Keywords | AttentionPoolMobileNeXt; Construction damage classification; Deep feature engineering; Image classification |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460203. Evolutionary computation |
| Byline Affiliations | Firat University, Turkey |
| School of Business | |
| School of Engineering | |
| University of New England | |
| University of Technology Sydney | |
| School of Mathematics, Physics and Computing |
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