## 6 Conclusions  
The global aim of the research is to contribute to the area of autonomous train movement, but also to intelligent detection of traffic signs and signals in general since the developed algorithm is widely applicable. The presented paper deals with a topic which, in addition to the fact that significant results already exist, is still in its infancy and which will be developed in the future and reach its full potential. As already pointed out in the paper, the biggest challenge in this area, in addition to the implementation itself, is the reliability and possibility of certification of developed systems, given that these are intelligent, and artificially controlled vehicles that need to move, travel, and participate autonomously on the railway. The main contribution of this paper should be the proposed system for detection of rail track and signals on the railway based on the integrated Hough-YOLO method. This system is based on the integration of traditional CV and AI into a single system of higher reliability and greater potential for possible certification. The connection between the detected rails and the signals next to them (on the right side) makes a hybrid system that provides the ability to be upgraded by including more signals and railway objects in a complex network of detection and subsequent decision-making based on the artificial intelligence.  
The current limitations of this paper relate to the size of the dataset used to train the CNNs, as well as the number of classes the system can detect. Future aspects of research on this topic relate primarily to the quantitative and qualitative improvement of the dataset by which CNNs will be trained, which will directly further improve the reliability of detection. Monitoring changes in detection reliability as the camera approaches the signal and introducing more and more classes will be applied.  
The validation metric method used was intersection over union (IoU). The results for all three networks were around 0.7. Calculated value of average precision was 0.89 and for recall, 0.76.  
The main direction of further research is the creation of a holistic system for the detection of railway objects. The holistic system for the detection of railway objects will be based on the multiple parallel connections between CV and CNNs detectors, with inclusion of railway digital maps containing the positions of all infrastructure elements.  
**Author Contributions:** Conceptualization, A.D.P. and M.B.; methodology, A.D.P., M.B. and G.A.; Python implementation, A.D.P. and G.A.; validation, M.B. and M.S.; formal analysis, D.S. and A.M.; investigation, A.D.P. and D.R.; resources, A.D.P. and D.S.; data curation, writing--original draft preparation, A.D.P.; writing--review and editing, A.D.P., M.B. and M.S.; visualization, A.D.P. and D.R.; supervision, M.B., D.S. and A.M.; project administration, A.D.P. and D.S.; funding acquisition, M.B., A.M. and M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors disclosed the receipt of the following financial support for the research, authorship, and/or publication of this article: This research received funding from the Shift2 Rail Joint Undertaking under the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 881784.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Special thanks to the Serbian Railways Infrastructure, and Serbia Cargo for the support in conducting the SMART2 OD field tests, as well as [PERSON] (YouTube channel dulevoz) for providing images adapted in this paper and for dataset creation.  
**Conflicts of Interest:** The authors declare no conflict of interest.