The safe operation of urban rail transit is of great significance to guarantee the life and wealth of the appointed people and maintain social security and stability. Due to the rapid increase in operating mileage and the continuous expansion of the scale of the network, the pressure on the safe operation of urban rail transit is increasing. In train operation failures, obstacles have a huge impact on the safety of driving. In many places, there are cases of casualties and economic losses caused by obstacles intruding into the safety clearance. Some scholars put forward the concept of automatic driving for auxiliary driving. This paper proposed an obstacle detection system based on background subtraction, including four modules: image retrieval module, rail recognition module, image alignment module and obstacle detection module. The system can achieve 92.7% accuracy and 90.1% recall with obstacle and achieve 94.8% recall without obstacle. And the system can reach rates around 8 fps, process images in real time.


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    Titel :

    Subway Obstacle Detection System Based on Machine Vision


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zhang, Zhenyuan (Herausgeber:in) / Lu, Taotao (Autor:in) / Geng, Chenge (Autor:in) / Dai, Honghui (Autor:in)

    Kongress:

    International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021



    Erscheinungsdatum :

    2022-06-01


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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