## 7 Conclusions  
This paper analyzed the problems of industrial sites where the sustainable application of a visual AI inspection system was difficult due to frequent changes in the environmental conditions and inspection targets, and developed technologies to solve these problems. We developed image acquisition technology using the mobile robot SPOT to obtain high-quality learning data and inspection images for the real-time visual inspection of the manual assembly process of automobiles. We proposed an AI system development architecture that could be effectively applied to industrial sites. We also improved the development of AI inspection algorithms by applying technologies to reduce the learning data acquisition period, save investment costs, improve algorithm performance, and automate the algorithm maintenance. This helped in drastically reducing the existing problems. In particular, the similarity of vehicle parts was used to develop the algorithm for new B-vehicle parts by utilizing the algorithm developed for A-vehicle parts, as shown in Figure 14. If the new B-vehicle parts were identical to the A-vehicle parts, the inspection could be performed using the A-vehicle part algorithm. If the new B-vehicle parts were not identical to the A-vehicle parts but similar, the development period of the algorithm could be reduced by transferring the learning of the B-vehicle parts to the A-vehicle part algorithm. Consequently, a vision AI system with the required detection performance during the early stages of production could be applied to detect assembly defects. Furthermore, it was possible to detect many defects caused by the low skill level of workers during the initial production of new cars in automobile production factories, which could significantly improve the quality of automobile assembly. Lastly, the effective AI technology development method proposed in this study will serve as a useful guide for the implementation of AI technology in industrial sites.  
Figure 14: Algorithm development is shortened through transfer learning with same/similar part algorithms and AI algorithm improvement through continuous accumulation of automobile assembly part image data.  
**Author Contributions:** Conceptualization, C.Y., D.K. and J.K.; Software, C.Y.; Validation, C.Y.; Formal analysis, J.K.; Investigation, J.K.; Resources, D.K.; Writing--original draft, C.Y.; Writing--review & editing, D.-S.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Hyundai Motor Company under project number 2021_CSTG_0174.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the first author due to Hyundai Motor Company's internal policies and therefore are not publicly available. Data access requests can be directed to the first author.  
**Acknowledgments:** We thank the Hyundai Motor Company for their support and resources provided for conducting this research.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper. However, [PERSON], [PERSON], and [PERSON] are employees of Hyundai Motor Company, which provided funding and technical support for this work. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.