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Assisting Weld Defect Detection Using U2Net: A Machine Learning Approach
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AI
DOI：10.1080/09349847.2025.2580247.png)
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Abstract
En 中文
Weld defect detection is usually performed by expert human interpretation of radiography images. This process is time consuming and difficult. Deep learning solutions have been developed to assist interpretation. These solutions often use a U-Net convolutional neural network to distinguish between defect and non-defect pixels in the image. Transformers, like those used for ChatGPT, are widely adapted for computer vision tasks. This work describes a UNet, called U2Net, using transformer and convolutional U-Nets to encode global and local information simultaneously for weld defect segmentation in radiographs. Results show that U2Net provides beneficial defect detection capabilities as compared to four evaluated U-Nets, underscoring the benefit of coupling global and local information in weld defect detection.
Keywords:
Weld defect
defect location
semantic segmentation
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