## 1 Introduction  
### The Significance and Development of Strip Surface Defect Detection  
Strip steel is one of the main products of the iron and steel industry. It is an indispensable raw material of shipbuilding, automobiles, machinery manufacturing and other industries. The quality of strip steel will directly affect the final product's quality and performance. In the strip steel manufacturing process, due to various factors such as raw materials, rolling equipment, and processing techniques, the surface of strip steel can develop different types of cracks, scarring, holes and other defects [1]. Strip steel surface defects not only cause serious production accidents such as strip breaking, stacking, and production line downtime, but also cause serious roll wear, with an immeasurable economic and social influence on the production enterprise [2]. More than 60% of strip steel product user quality objection events in China are caused by surface defects. Therefore, it is very important to improve the quality of strip steel by detection of these defects in the rolling process in time, adjustment of control parameters, classification, and labeling of different strip steel levels.  
In traditional iron and steel enterprises, the surface quality of high-speed moving strip steel is usually detected by an artificial naked eye stroboscopic method. However,this method also has its shortcomings. For example, stroboscopic lighting can only be used on some specific strip surfaces. Due to visual fatigue, detection timeliness is poor, and the missed detection and misdetection rate are high. For products surface defect detection carried on a conveyor belt, the human eye can detect only 60% of the surface defects, and the width of the product cannot exceed 2 m and, the moving speed of the products cannot exceed 30 m/s, in the best case [3]. At present, many Chinese metallurgical enterprises still use manual visual inspection and sampling inspection of strip steel surfaces with low efficiency and poor detection efficiency.  
In recent years, with the development of industrial technology, machine vision is gradually being used more often in enterprises as a non-contact and non-destructive testing technology. Machine vision has a high resolution, is highly classified, is little affected by the ambient electromagnetic field, and is low cost. By deploying the image acquisition apparatus online and sending the real-time collection images to a monitoring device, inspectors can achieve real-time monitoring of the metal surface.  
The Practical Difficulties of Using the Machine Vision Surface Detection Technique on Imbalanced Datasets  
The key to machine vision surface detection technique is developing various types of defect detection algorithms. Due to strip steel's rapid movement on the production line, it will produce a large amount of image data (for example, 25 frames/sec), which demands an excellent real-time defect detection algorithm. It also can lead images captured by CCD cameras to be of low resolution and less qualified. Since the defect images captured by CCD cameras fall into numerous categories, are complex, and have boundary overlap, it is typical to have an imbalanced dataset with a small number of samples. When the data has a long-tail distribution, it can lead to bias in the classifier. The classifier is more inclined to identify classes with a sufficient sample size and rich diversity [4; 5]. However, it is unfair to ignore other classes. Traditional machine learning algorithms do not have enough data, either in terms of quantity or quality, to train a model.  
Standard machine learning algorithms are based on the assumption that the dataset has a sufficient number of samples and balanced class distribution. Uneven distribution in a few samples dataset causes tremendous difficulties for standard algorithm application. Algorithms prefer that the categories contain plentiful samples and that each category contains a similar number of samples.