Dilated convolution modules and Squeeze-and-Excitation self-attention mechanisms expand the convolutional receptive field and improve feature extraction capabilities. The fully connected layer is replaced by a global average pooling layer, reducing training parameters and mitigating overfitting. Additionally, combining DropBlock technology with Batch Normalization optimizes the feature extraction process, enhancing generalization ability.
The experimental results demonstrate that the proposed model achieves a recognition accuracy of 98.78% and exhibits superior performance in terms of efficiency and lightweight design, highlighting its potential for deployment in real-world industrial applications.
Welding is an important processing method and is widely used in shipbuilding, automobile manufacturing, aerospace, and equipment manufacturing. In view of the limitations of the production environment and processes, various defects are inevitably produced at the welds. The service life and performance of structural components are affected by the quality of welding. In severe cases, it can lead to personal casualties or property loss, and in minor cases, it can lead to the collapse of the system. Therefore, Non-destructive testing (NDT) of welded components is very necessary. It can prevent some unqualified products from entering the market, ensure personal safety, and protect economic benefits. Among the many NDT methods, X-ray non-destructive testing is a commonly used method for welding defects.
Read the full article at Nature.com.