Image Enhancement of Steel Plate Defects Based on Generative Adversarial Networks

Abstract

In this study, the problem of a limited number of data samples, which affects the detection accuracy, arises for the image classification task of steel plate surface defects under conditions of small sample sizes. A data enhancement method based on generative adversarial networks is proposed. The method introduces a two-way attention mechanism, which is specifically designed to improve the model’s ability to identify weak defects and optimize the model structure of the network discriminator, which augments the model’s capacity to perceive the overall details of the image and effectively improves the intricacy and authenticity of the generated images. By enhancing the two original datasets, the experimental results show that the proposed method improves the average accuracy by 8.5% across the four convolutional classification models. The results demonstrate the superior detection accuracy of the proposed method, improving the classification of steel plate surface defects.