Unveiling Patterns: A Study on Semi-Supervised Classification of Strip Surface Defects

As a critical intermediate material in the iron and steel industry, strip steel will inevitably have various surface defects during its processing, which directly affects the service performance and life of the material. Therefore, the classification technology of strip surface defects has always been the focus of research. Currently, combining computer vision with deep learning is often used to classify the surface defects of strip steel, which usually runs in full supervision mode. However, the performance of the complete supervision method depends mainly on the quality and quantity of labeled samples. At the same time, in industrial scenes, there are few labeled samples available, and most even have no labels, which seriously restricts the performance of the traditional full supervision model. This paper introduces the idea of semi-supervised learning, and a new semi-supervised classification model of strip surface defects is proposed to alleviate the degradation of model classification performance caused by insufficient labeled samples. Specifically, a new image synthesis model (ISM) is proposed in this paper. By improving the loss function of the discriminator, the generated false samples are more realistic. In addition, this paper also presents a double uncertainty weighting technique (DUW), which weighs the loss of misclassified samples in a more detailed way, thus realizing fine adjustment of the model. This method can fully mine the potential feature information in unlabeled samples and further improve the performance and generalization ability of the model. In this paper, we use the NEU-CLS dataset to test our model. When only 10% and 90% of labeled and unlabeled samples are used for training, the classification accuracy reaches 91.14%, fully proving this method’s practicability and superiority.