Defect detection algorithm for image reconstruction based on the self-attentive mechanism

Abstract

In this paper, we propose an image reconstruction method for defect detection, which introduces a self-attentive mechanism into the generative adversarial neural network to make it capable of extracting global features in response to the weak ability of the generative adversarial neural network to establish image remote dependencies. To address the problem that the adversarial loss function is not suitable for network training, a pixel-by-pixel loss function is introduced to constrain the image generated by the generator so that the output does not deviate too much from the true value during training. The experimental results show that the trained GAN network can effectively reconstruct the defective areas to separate the defects from the background.