## 1 Introduction  
In the manufacturing sector, any process needs continuous monitoring to ensure their behavior and performance. In the case of components manufacturing for sub-sectors such as the automotive industry, quality inspection is an imperative as the final quality of the components can affect their functionality. Component manufacturers are continuously looking for innovative quality inspection mechanisms that are the most efficient and cost-effective strategy in order to survive in a very competitive market.  
The Industry 4.0 paradigm has brought great opportunities to improve and manage quality control processes through the application of new visual computing related technologies such as computer vision or artificial intelligence [1].  
This work focuses on steel quality control. Steel has multiple uses in the construction of buildings and infrastructures or in the manufacturing sector [2]. In the manufacturing sector, the quality control processes carried out on steel products comprises the following phases according to the defects to be located:  
* A visual analysis by the operator (Superficial defects);* Studies on its chemical composition (Structural defects);
* Studies on its mechanical characteristics (Structural defects);
* Study of geometric characteristics (Dimensional defects).  
The component quality control process has traditionally been done by applying statistical process control (SPC), where various evaluation tests are applied to a component sampled from the production line at a given sampling frequency. Test results are then compared with acceptance/rejection criteria established according to the regulations or customer requirements. Nowadays, due to the competitiveness of the markets, customer requirements are becoming more and more demanding, aiming to achieve 100% inspection of the production instead of SPC.  
In the steel component production workflow, surface defects can occur at several stages. The wide variety of surface defects that can occur for all different existing steel products makes classification difficult. However, most of these defects involve some type of surface roughness.To cope with the needs to inspect 100% of the production at high production rates, and the new requirement of maximum precision in the detection of defects, Deep Learning (DL) based models are becoming the predominant approach to tackle the automatic defect inspection problem [3]. Surface inspection data from the manufacturing industry are usually highly imbalanced because defects are typically low frequency events. It is easy to understand that, for a company to be profitable, most of the manufactured products must be free from defects. Therefore, a database extracted directly from the production line will be composed mostly of non-defective products. Using such type of datasets can lead to unwanted results, due to the extreme imbalance of the classes in the data [4; 5]. Machine learning model building approaches generally have a strong bias towards the majority class in the case of imbalanced datasets. In order to correct this effect, the generation of synthetic samples of the minority class is often used to obtain a better balanced dataset [6]. The generation of realistic images containing surface defects can not be easily achieved by conventional data augmentation methods.