Abstract: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 CSWin transformer-skip (CSW-S) is proposed to classify concrete cracks. The method is optimized by adding residual links to the existing CSWin transformer network and then trained and tested using a dataset with 17,000 images. The experimental resul… Show more
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