Abstract
Rail trains are an indispensable part of the transportation, so technological innovations such as artificial intelligence will inevitably promote the overall development of unmanned driving technology in this field, how to ensure the safety of unmanned trains is related to intelligent rail detection technology is very important, such as the detection of foreign body intrusion on rails, turnouts, curved rails, etc. These technologies have become the basic technology of rail train automatic driving technology. This paper divides the key technology of rail intelligent detection into three parts, and conducts in-depth analysis and research from these three aspects: curved track detection and warning module, turnout detection and warning module, obstacle detection and warning module, and combined with the actual needs of each module, Continuously through experiments, choose more suitable and optimal algorithms to achieve and then integrate the module algorithms to achieve the synchronization of the work between each module without interfering with each other, and design and implement the intelligent rail detection and early warning system to enhance the interactive experience. According to the functional requirements of different modules of the system, this paper selects core algorithms such as curvature radius, Hough transforms and frame difference method to improve each module, and verifies the feasibility of the selected algorithm.
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Acknowledgement
This work was supported by the National Nature Science Foundation of China (Grant No. 61702347), Natural Science Foundation of Hebei Province (Grant No. F2017210161), Science and Technology Research Project of Higher Education in Hebei Province (Grant No. QN2017132).
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Zhang, Y., Guo, W. (2021). Intelligent Detection and Early Warning System of Railway Track. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_43
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DOI: https://doi.org/10.1007/978-3-030-78609-0_43
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