## 2 State-of-the-Art  
The recent advancements in machine learning algorithms and the increase in computational power, as well as the smart manufacturing strategies brought by Industry 4.0, have led to the development of computer vision based solutions to address the multiple facets of automotive production. These can range anywhere from the replacement of expensive sensors with cameras to visual inspection in manufacturing and assembly, along many other fields outside the production industry [11]. It is such a profitable field, with such a high demand for cutting-edge solutions, that commercial companies develop several hardware, software and services specifically to answer the demanding conditions and needs of the industrial sector. The quality control performed through visual inspection is an extremely important part of the production process. For example, in fault detection, machine and deep learning algorithms are used to visually identify the occurrence of quality issues, or other structural flaws in part manufacture and/or assembly. Detection of such faults helps not only with the correction of the error as soon as it occurs, but its classification can diagnose its exact cause, thus locating the machine failure that provoked it [12]. Some examples include the classification of car seat backrests through the combination of speeded up robust features (SURF) and a convolutional neural network (CNN) by [PERSON] et al. [13], and the inspection of defects such as cracks, tears or unwanted inclusions, and other elements, via region based CNN (R-CNN) with a modified stochastic gradient descent with momentum (SGDM) by [PERSON] et al. [14]. As an example of fault detection done on welding and solder joints, [PERSON] and [PERSON] [15] suggest an inspection on door panels through a machine learning algorithm combined with template matching (based on the Canny edge detector and sequential similarity detection) for the former and the Hough transform and image segmentation for the latter. The classification of surface defects, on the other hand, focuses on the inspection of fabrics, glass, painted surfaces, etc. to detect any scratches, dents and other minor imperfections that impact the aesthetic quality of the vehicle, and thus can seriously impact the brand's image and consumer opinion. This task can be particularly challenging, as many of the defects can be quite small, faint and irregular in shape, hidden in the highly reflective surfaces and by the uneven lighting conditions [16]. Examples range from the detection of tiny defects on paint surfaces with a combination of a deep convolutional neural network (DCNN) and YOLOv3 by [PERSON] et al. [17], to the detection of multiple manufacturing defects on wheel hubs (such as scratches, indentations or oil pollution) through the use of a Faster R-CNN by [PERSON] et al. [18]. However, despite the constant strides in the field towards automation, workers still play an important role in the production process. Therefore, there is a strong investment in the creation of a multitude of tools to help reduce their workload and increase their productivity [19]. Many are simple maintenance software tools that provide workers with information on that specific vehicle model, often in the form of augmented reality (AR), or with instruction manuals on the parts and procedures. Some examples are the AR smart devices developed by Volkswagen, Bosch, Hyundai and Ferrari [19; 20], or the research done by [PERSON] et al. [21] on the development of an AR markerless tracking system of multiple car parts, as the base for training and maintenance solutions. Another form of inspection software are devices developed to support workers in quality control tasks. These replace the previous paper description of the vehicle with a more flexible, responsive and efficient solution that can communicate directly with the smart factory [1; 5]. Ford, for example, has developed a smartphone app that serves as a wrist-worn Portable Quality Assurance Device [22].