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Link to original content: https://doi.org/10.1007/s13198-021-01509-w
Road condition judgment system of railway transportation based on artificial intelligence recognition technology | International Journal of System Assurance Engineering and Management Skip to main content
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Road condition judgment system of railway transportation based on artificial intelligence recognition technology

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Abstract

In order to improve the recognition effect of road condition of railway transportation and improve the safety of railway transportation, this paper combines artificial intelligence recognition technology to improve the algorithm, and applies Minkowski distance and dynamic time warping distance to the evaluation of the accuracy of tram track recognition. Moreover, this paper introduces the mean value and variance of the distance between track points as auxiliary evaluation indicators, establishes the evaluation indicators for the accuracy of tram track recognition, and proposes a road condition of railway transportation judgment system based on artificial intelligence recognition technology. In addition, this paper installs a high-definition camera and a visible light sensor on the head of the train to analyze and process the collected smart video and visible light images respectively. Finally, this paper verifies the system of this paper through experimental research. From the experimental research, it can be seen that the system constructed in this paper can effectively improve the effect of road condition of railway transportation recognition.

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There was no outside funding or grants received that assisted in this study.

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Correspondence to Liang Cao.

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The author declared that they have no conflicts of interest to this work. I declare that I do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Cao, L. Road condition judgment system of railway transportation based on artificial intelligence recognition technology. Int J Syst Assur Eng Manag 14, 718–727 (2023). https://doi.org/10.1007/s13198-021-01509-w

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  • DOI: https://doi.org/10.1007/s13198-021-01509-w

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