## 5 Conclusions and Future Research  
We proposed replacing the traditional paper-based conformity list, a limited approach still frequently used in quality control in the highly automated automotive industry, with a digital version. With this we tackle two major issues, mental and physical fatigue, since the worker no longer needs to have a detailed mental image of all the items on the lists, nor needs to physically access a terminal at the end of the assembly line to submit the report, which also represents an increased inspection time. Our fully hybrid quality control system aims to integrate the automated detection of the vehicle parts with an information visualization interface to present the digital conformity list to the worker, allowing them to verify the non-conforming parts and wirelessly submit the final report. One of the core building blocks of both modules is a 3D vehicle generator that is used to generate the vehicle parts for the digital conformity list and to train the automated detection system. The use of simulated data allows us not only to have unlimited amounts of data with sufficient variability regarding environment conditions, but also to automatically annotate the images while avoiding the time and resource consuming process of data collection and manual labeling. We show that, through incremental improvements upon a hierarchical object detection baseline trained solely using simulated data, which included domain adaptation of simulated images and pseudo labeling of real images, we are able to improve by 11 pp the baseline and achieve a final 0.565 example-based F1-score on real images in an unconstrained scenario, without any recourse to annotated real data. Moreover, we simulated a fixed camera setup in which data collection would be performed in a very controlled environment and there would be one object detector for each view of the vehicle, and validated \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Inspector 1** & **Inspector 2** \\ \hline Perceived Usefulness & \(4.83\pm 0.37\) & \(4.50\pm 0.50\) \\ Perceived Base of Use & \(4.50\pm 0.00\) & \(4.00\pm 0.00\) \\ Right Data and Level of Detail & \(5.00\pm 0.00\) & \(4.00\pm 0.00\) \\ Ease of Software Use & \(5.00\pm 0.00\) & \(4.50\pm 0.50\) \\ Look and Feel & \(5.00\pm 0.00\) & \(3.50\pm 0.50\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean and standard deviations of the quantitatively measurable criteria of the TAM and TIF questionnaire, presented to quality control inspectors for validation of the information visualization module. the viability of this solution, having improved the baseline by 14.6 pp (corresponding to 0.601 F1-score). Furthermore, the internal validation questionnaire conducted with the factory's quality control experts confirmed the extreme usefulness of this solution, especially when compared to the paper one. They found the support tool to be very fast and easy to use, its design simple and intuitive enough for them to figure out how to navigate and report the non-conformities without any prior training. Testing the proposed system on the factory floor, rather than in a simulated environment, would be necessary to confirm how much this solution would improve the worker's inspection performance. However, the experts showed absolute certainty that the support tool would greatly ease the documentation and submission of the reporting process and prove to be a major asset in aiding their quality control inspection tasks. The lack of bounding box annotations for the real data, coupled with the fact that this work constitutes a preliminary validation before testing on the factory floor, are the main, current limiting factors of the proposed solution. As such, the immediate next step is to obtain the aforementioned fine grained annotations and to perform extensive testing on the factory floor and compare the mobile and fixed camera scenarios.