# Augmented Reality-Based Framework Supporting Visual Inspection for Automotive Industry
## 3 The Proposed Cognitive System for Inspection
### Cognitive Assistant System Conception and Design
#### 3.2.2 Data Visualization through the Cognitive Assistant System  
Cognitive Assistants support humans and enhance their capabilities in solving a wide variety of complex tasks. In this section, we introduce a cognitive assistant that is developed using AR technology aiming at improving the operator's performance and quick decision-making abilities. In other words, assisting the operator by displaying the defect and locating it to assist the decision of fixing the defects of the scanned vehicle on the final station.  
Hence, the developed AR application displays a UI including the list of tasks to be inspected. The operator, performing the check, reads the controls that appear on the display without taking his eyes off the object of the inspection while reducing the inspection time and avoiding errors that can occur if some elements are overlooked. Based on the given scenario detailed in Section 3.1 and based on the current inspection interface (see Figure 2), we have proposed a UI where the operator would be able to visualize all the defects in one table. For the ergonomic reason, we have chosen to display the list of controls and errors, which need to be corrected by the operator, in one table differentiating them with different colors as follows: red for errors and blue for controls. As mentioned before, for UI conception and implementation we have selected Unity 3D as a development platform. However, by integrating this main platform with an augmented reality software development kit, the proposed system becomes able to detect an AR marker (the vehicle identifier), appearing in the field of view of the camera, and project a virtual table with the corresponding tasks into the real-world. In our case, adopting the marker-based AR is crucial and more accurate since the vehicle has already an identifier in a QR or bar code format. Another advantage of marker-based AR use is the reduced computation cost compared to the other type of AR when the marker is defined correctly [14]. The idea behind the use of Vuforia is that such technology can detect accurately these formats of marker supporting at the same time any device with a camera [30]. In the literature, research has found that Vuforia is the best to use for AR applications since 2018 [31]. First, the time needed to run an AR application using Vuforia is usually less than using other SDKs such as AR toolkit. Second, in the image tracking studies, the tracking time remains constant with increasing distance, so the distance between the recognition image and the camera does not affect the tracking. Finally, the tracking times obtained with three different devices at different distances are often the lowest [14].  
Hence, through the proposed system, the operator can validate the state of checked and corrected errors using a touch input method. In front of each error in the list, buttons are designed to be clicked, when the task is performed by the operator. The button click generates the removal of the task from the UI, but it remains saved in the system for final transmission and save to the MES. Figure 4 details the way of implementation of the above-described scenario using the 4C (Computation, Communication, Coordination, and Configuration) reference model [32].