The invention discloses an elevator door position and opening and closing state detection method and device, a medium and a terminal. The method comprises the following steps that an elevator door detection network model is trained based on a first-order full convolution target detection algorithm; and real-time images of the elevator door are collected, and the position and the opening and closing state of the elevator door in the real-time images of the elevator door are recognized based on the trained elevator door detection network model. On the basis of the first-order full convolution target detection algorithm (FCOS), a prior frame does not need to be set in the neural network training and detection process, the number of parameters is remarkably reduced, a large amount of calculation is avoided, memory occupation is reduced, and the training and detection process is simple and efficient; and meanwhile, after detection is completed, confidence coefficient calculation is conducted through a weighted post-processing method, so that the number of redundant frames is reduced, and the accuracy of elevator door position and opening and closing state detection is improved.

    本发明公开了一种电梯门位置和开关状态的检测方法、装置、介质和终端,方法包括以下步骤:基于一阶全卷积目标检测算法训练电梯门检测网络模型;采集电梯门实时图像,并基于训练完成的所述电梯门检测网络模型识别所述电梯门实时图像中电梯门的位置和开关状态。本发明基于一阶全卷积目标检测算法(FCOS),在神经网络训练和检测过程中无需设置先验框,不仅显著减少了参数数目,而且避免了大量计算,减少了内存占用,训练和检测过程简单高效;同时在检测完成后,采用加权的后处理方法进行置信度计算,从而减少冗余框的数量,提高了对电梯门位置和开关状态检测的准确性。


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    Titel :

    Elevator door position and opening and closing state detection method and device, medium and terminal


    Weitere Titelangaben:

    电梯门位置和开关状态的检测方法、装置、介质和终端


    Beteiligte:
    QIN HAO (Autor:in) / ZHAO MING (Autor:in)

    Erscheinungsdatum :

    2021-09-24


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06K Erkennen von Daten , RECOGNITION OF DATA / B66B Aufzüge , ELEVATORS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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