X-ray nondestructive testing technology is widely used for inspecting welds and identifying defects, and is crucial in the manufacturing industry. However, the diversity of welding defects and the imbalanced defect samples reduce defect classification model accuracy and can cause classifier overfitting. This paper proposes an improved DCGAN model for generating welding defect samples by integrating deep convolutional neural networks to enhance the training relationship between the generator and discriminato
Source: NDT
