The method comprises the following steps: based on a monitoring video of an actual fixed camera position on an airport departure lane side, using a YOLOv4 algorithm to identify vehicle and pedestrian targets, and using a DeepSORT algorithm to track the targets; and the running state of the vehicle and the getting-on and getting-off behaviors of passengers are judged according to the target category returned by the YOLOv4 and the target position information recorded by the DeepSORT, the change condition of the number of passengers in the vehicle is counted when the vehicle stops, whether the vehicle has the passenger receiving behavior or not is judged according to the statistical result, and recognition of the violation vehicle on the departure floor of the airport is completed. The method has the beneficial effects that the accuracy can reach 83.3%, the detection speed is greatly improved compared with a traditional manual identification mode, the workload of monitoring law enforcement personnel can be greatly reduced, the law enforcement efficiency and law enforcement accuracy of off-site law enforcement are improved, and the method has a wide development prospect in the field of intelligent traffic supervision and has a wide application prospect. The safety management of airport landside traffic can be further enhanced, and effective guarantee is provided for the development of the traffic industry.

    本发明公开一种基于深度学习的机场出发层违章接客车辆识别方法:基于机场出发车道边实际固定机位的监控视频,使用YOLO_v4算法识别车辆与行人目标,利用DeepSORT算法跟踪目标,根据YOLO_v4返回的目标类别与DeepSORT记录的目标位置信息判断车辆的运行状态与乘客上下车行为,在车辆停止时统计车内人数变化情况,根据该统计结果判断车辆是否存在接客行为,完成对机场出发层违章车辆的识别。有益效果:准确度可以达到83.3%,检测速度相较传统人工识别方式提升巨大,可以大大减小监控执法人员的工作量,提高非现场执法的执法效率及执法准确性,在智能交通监管领域具有广阔的发展前景,可以使机场陆侧交通的安全管理得到进一步强化,为交通行业的长足发展提供有效保障。


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    Titel :

    Airport departure layer violation vehicle automatic identification method based on deep learning


    Weitere Titelangaben:

    一种基于深度学习的机场出发层违章车辆自动识别方法


    Beteiligte:
    BAI QIANG (Autor:in) / SHAO YUQI (Autor:in) / MENG SIYUAN (Autor:in) / WANG YUXUAN (Autor:in) / QIN QIAN (Autor:in) / DU MAOWEI (Autor:in) / HUANG MING (Autor:in)

    Erscheinungsdatum :

    2022-07-29


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06V / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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