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امروز شنبه 1403.10.1 Iranian Construction Engineering and Management
صفحه اصلی
مقالات و آثار علمی

مقاله تشخیص ترک بتن با استفاده از بینایی رایانه ای SWIN U-Net Approach

Authors
Ali Sarhadi, Mehdi Ravanshadnia, Armin Monirabbasi, Milad Ghanbari
Publication date
2024/5/20
Journal
IEEE Access
Publisher
IEEE
Description
Utilizing convolutional neural network (CNN) models, computer vision technology has become a reliable and powerful tool for detecting potential damage in concrete structures at the pixel level. In this study, an advanced SWIN U-Net architecture was introduced to detect concrete cracks. The model integrated attention-based convolutional neural networks to enhance the speed and accuracy of crack detection significantly. The distinctive features of the SWIN Transformer made the application of the model to images of varying sizes possible while the computational resources were used efficiently. To train the model, a dataset consisting of crack images, each accompanied by a corresponding mask that highlighted the relevant regions within the image, was used. The training data were augmented using Flip, Rotate, Random Contrast, Random Gamma, Random Brightness, Elastic Transformation, Grid Distortion, and …


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