## 5 Conclusions
The first publications utilizing vision transformer models occurred at the lower bound of this gap in the first quarter of 2022, while more followed over the course of the year. Since their results are in the upper performance range of their respective AVI use cases, and much effort is being put into reducing their hardware requirements, we expect them to become the standard in the future. A more thorough investigation of vision transformers for classification and attention-enhanced CNNs, DDETR, and custom transformer models for localization in more different use cases could yield insights if these models live up to the potential they have shown in their low number of applications. This review paper is not without limitations, which should be acknowledged in order to fully assess its scope and implications. The first limitation concerns the use of exclusively 2D image data. While this choice was motivated by broad applicability, it inadvertently limits the integration of more sophisticated imaging technologies. Nevertheless, it is important to emphasize that input signals other than traditional RGB images limit the effectiveness of domain adaptation techniques, e.g., transfer learning, due to a more pronounced domain gap. Another limitation of our literature review is the concise treatment of the models and training methods used in the reviewed publications. Given the extensive nature of the publication corpus considered, it could distort the research scope that we wanted to address by providing a detailed exposition of each model and training methodology. It should be noted that all of the included literature sources are open-access, allowing interested readers unrestricted access to the publications themselves, where more detailed descriptions of these aspects can be found. These limitations provide avenues for future research to delve deeper into the topics addressed herein and extend the knowledge base in the field. Therefore, a future addition to our work will be looking at other input sensors for AVI. For example, an interesting research question to investigate is, To what extent can approaches used for 2D images be transferred to 3D images, containing depth information, or other sensor outputs, such as point clouds? Another direction for future research is to investigate the time gap between academic research and industrial applications, which we assume is not limited to AVI. The reasons are probably manifold, but is it possible to reduce the identified gap of two to three years? And what is necessary to reduce the gap? We expect vision transformers to become the models of choice for AVI applications. However, most publications focused on fine-tuning pretrained models in a supervised manner, ignoring their strong self-supervised learning capabilities. These have been demonstrated on benchmark datasets for specific tasks, such as object detection, as well as for learning more general representations transferable to a multitude of tasks. From our point of view, the transfer of self-supervised learning to AVI applications seems to be a research path worth spending more attention on in the future, especially because the available datasets for industrial applications are much smaller than the benchmark datasets. **Author Contributions:** Conceptualization, N.H. and M.A.G. ; Methodology, N.H. and M.A.G. ; Resources, T.M. ; Data Curation, N.H., M.A.G., K.A. and F.H. ; Writing--Original Draft Preparation, N.H., M.A.G., K.A. and F.H. ; Writing--Review, R.M. and T.M. ; Writing--Editing, N.H. and M.A.G. ; Visualization, N.H. and M.A.G. ; Supervision, T.M. ; Project Administration, R.M. ; Funding Acquisition, R.M. and T.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the German Federal Ministry for Digital and Transport in the program \"future rail freight transport\" under grant number 53T20011 UW. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflicts of interest.