# Steel Surface Defect Classification Based on Small Sample Learning
## 1 Introduction  
In the hot rolling manufacturing process of strip steel, defects may occur on its surface due to processing technology, mechanical equipment and human errors. These defects will greatly change the mechanical properties of steel and weaken its quality. The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects [1]. For example, rolled in scale is probably caused by severe peeling of the oxide film of the stand roll before rolling is finished, while the scratches are caused by the protrusions of the strip in the area of the rolling line, or the friction between the fixed roll and the strip surface. Traditionally, the surface quality of steel is detected by human observation. However, it depends on the worker's skill and experience, and continuous work will reduce the inspection accuracy and cause great harm to the worker's health. Meanwhile, defective samples are very scarce in the hot rolling process, which poses a challenge to the classification of steel surface defects. Traditional machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree, can only learn some low-level features rather than detailed and abstract features. When two kinds of defects are similar, traditional machine learning algorithms may fail to classify them. The core of deep learning is an artificial neural network. Similar to human nerves, artificial neural networks are composed of thousands of artificial neurons, with numerous nodes and tens of thousands of parameters that need to be learned. Generally speaking, when the number of samples is small, the model will not be able to learn the correspondingfeatures, and even overfitting will occur. In recent years, more and more researchers have been devoted to the field of small sample learning. Generally, there are four methods in the field of small sample learning, as described below. Method based on meta-learning. It trains a meta model to learn the knowledge of different tasks, so that the trained model can be quickly generalized on new tasks, such as the Model-Agnostic Meta-Learning algorithm (MAML) proposed by [PERSON] [2] and the Long Short Term Memory network (LSTM) proposed by [PERSON] [3]. The existing meta-learning methods mostly use an LSTM or Recurrent Neural Network (RNN) structure in the model, but the disadvantages are high time complexity and slow running speed. Therefore, it is not suitable for industrial application. Method based on data enhancement. Since the small sample problem is caused by lack of samples, it can be solved by expanding the sample set. Inspired by this idea, models such as Generative Adversarial Networks (GAN) [4] have been developed. However, because the expanded samples are transformations of the original images, they are similar to the original images. Method based on fine-tuning. This model is usually pre-trained on a large-scale dataset, after which the main layers of the entire network are frozen [5; 6] and, finally, the parameters of the full connection layers or the top layers of the neural network model are fine-tuned on the target dataset. The limitation of this method is that it requires a certain similarity between the target dataset and the pre-trained source dataset. Because the characteristics of the samples in the existing large-scale datasets are very different from the characteristics of the steel surface defects, it is difficult to deal with the classification of steel surface defects in this method. Method based on metric learning. Metric Learning is also called similarity learning or distance metric learning. It maps the features of the image to a new feature space. In this new feature space, a special measurement method is used to make the distance between similar samples as short as possible and the distance between heterogeneous samples as long as possible. Compared with the above three methods, its advantage is that the main goal of learning is the similarity between samples.