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An improved transformer-based concrete crack classification method
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AI
DOI：10.1038/s41598-024-54835-x.png)
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摘要
En 中文
In concrete structures, surface cracks are an important indicator for assessing the durability and serviceability of the structure. Existing convolutional neural networks for concrete crack identification are inefficient and computationally costly. Therefore, a new Cross Swin transformer-skip (CSW-S) is proposed to classify concrete cracks. The method is optimized by adding residual links to the existing Cross Swin transformer network and then trained and tested using a dataset with 17,000 images. The experimental results show that the improved CSW-S network has an extended range of extracted image features, which improves the accuracy of crack recognition. A detection accuracy of 96.92% is obtained using the trained CSW-S without pretraining. The improved transformer model has higher recognition efficiency and accuracy than the traditional transformer model and the classical CNN model.
Keyword:
Image feature extraction
Transformer
Deep learning
Structural health monitoring
Crack detection
AI总结
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期刊
IF:
3.9
论文数:
27.9W
被引数:
83.5W
机构
引用论文
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete深度卷积神经网络和边缘检测器在基于图像的混凝土裂缝检测中的比较
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