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A platform for research: civil engineering, architecture and urbanism
Attention enhanced EfficientNet for concrete structure crack classification with generative adversarial network augmented data
https://orcid.org/0000-0001-5887-0517
https://orcid.org/0009-0003-4012-8180
AbstractCracks are an important indicator of the decline in the load‐bearing capacity of buildings. Therefore, it is of great significance to detect and classify cracks in reinforced concrete (RC) building exterior walls. Accurately and automatically classifying cracks remains challenging due to the highly irregular nature of crack images, irregular lighting conditions, and background texture noise. An EfficientNet network model combined with a HiLo attention mechanism was proposed to achieve precise identification and classification of RC exterior wall cracks. Firstly, existing crack datasets were categorized, and a combination of classical data augmentation and conditional generative adversarial networks was used to augment the data, improving the model's generalization ability under different imaging conditions and mitigating the adverse effects of the unbalanced dataset. Furthermore, crack images were divided into high‐frequency (Hi‐Fi) and low‐frequency (Lo‐Fi) feature maps by applying the HiLo attention mechanism. Hi‐Fi module suppressed background noise and captured the edge details of cracks by retaining relatively high‐resolution feature maps. Lo‐Fi module extracts global features of cracks through window segmentation and average pooling operations, thereby enhancing the classification capability of model. The experimental results showed that the HiLo‐EfficientNet model achieved the best image classification performance with an overall classification accuracy of 91.63% compared to the original and mainstream deep learning models.
Attention enhanced EfficientNet for concrete structure crack classification with generative adversarial network augmented data
https://orcid.org/0000-0001-5887-0517
https://orcid.org/0009-0003-4012-8180
AbstractCracks are an important indicator of the decline in the load‐bearing capacity of buildings. Therefore, it is of great significance to detect and classify cracks in reinforced concrete (RC) building exterior walls. Accurately and automatically classifying cracks remains challenging due to the highly irregular nature of crack images, irregular lighting conditions, and background texture noise. An EfficientNet network model combined with a HiLo attention mechanism was proposed to achieve precise identification and classification of RC exterior wall cracks. Firstly, existing crack datasets were categorized, and a combination of classical data augmentation and conditional generative adversarial networks was used to augment the data, improving the model's generalization ability under different imaging conditions and mitigating the adverse effects of the unbalanced dataset. Furthermore, crack images were divided into high‐frequency (Hi‐Fi) and low‐frequency (Lo‐Fi) feature maps by applying the HiLo attention mechanism. Hi‐Fi module suppressed background noise and captured the edge details of cracks by retaining relatively high‐resolution feature maps. Lo‐Fi module extracts global features of cracks through window segmentation and average pooling operations, thereby enhancing the classification capability of model. The experimental results showed that the HiLo‐EfficientNet model achieved the best image classification performance with an overall classification accuracy of 91.63% compared to the original and mainstream deep learning models.
Attention enhanced EfficientNet for concrete structure crack classification with generative adversarial network augmented data
https://orcid.org/0000-0001-5887-0517
https://orcid.org/0009-0003-4012-8180
AbstractCracks are an important indicator of the decline in the load‐bearing capacity of buildings. Therefore, it is of great significance to detect and classify cracks in reinforced concrete (RC) building exterior walls. Accurately and automatically classifying cracks remains challenging due to the highly irregular nature of crack images, irregular lighting conditions, and background texture noise. An EfficientNet network model combined with a HiLo attention mechanism was proposed to achieve precise identification and classification of RC exterior wall cracks. Firstly, existing crack datasets were categorized, and a combination of classical data augmentation and conditional generative adversarial networks was used to augment the data, improving the model's generalization ability under different imaging conditions and mitigating the adverse effects of the unbalanced dataset. Furthermore, crack images were divided into high‐frequency (Hi‐Fi) and low‐frequency (Lo‐Fi) feature maps by applying the HiLo attention mechanism. Hi‐Fi module suppressed background noise and captured the edge details of cracks by retaining relatively high‐resolution feature maps. Lo‐Fi module extracts global features of cracks through window segmentation and average pooling operations, thereby enhancing the classification capability of model. The experimental results showed that the HiLo‐EfficientNet model achieved the best image classification performance with an overall classification accuracy of 91.63% compared to the original and mainstream deep learning models.
Attention enhanced EfficientNet for concrete structure crack classification with generative adversarial network augmented data
Structural Concrete
Lin, Xumei (author) / Wang, Shiyuan (author) / Shen, Jiahui (author)
2025-03-03
Article (Journal)
Electronic Resource
English
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