Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network

Defect inspection is very important for guaranteeing the surface quality of industrial steel products, but related methods are based primarily on supervised learning which requires ample labeled samples for training. However, there can be no doubt that inspecting defects on steel surface is always a data-limited task due to difficult sample collection and expensive expert labeling. Unlike the previous works in which only labeled samples are treated using supervised classifiers, we propose a semi-supervised learning (SSL) defect classification approach based on multi-training of two different networks: a categorized generative adversarial network (GAN) and a residual network. This method uses the GAN to generate a large number of unlabeled samples. And then the multitraining algorithm that uses two classifiers based on different learning strategies is proposed to integrate both labeled and unlabeled into SSL process. Finally, through the multiple training process, our SSL method can acquire higher accuracy and better robustness than the supervised one using only limited labeled samples. Experimental results clearly demonstrate that the effectiveness of our proposed method, achieving the classification accuracy of 99.56%.