The invention relates to a traffic jam monitoring method, device and equipment and a storage medium, and is applied to the field of deep learning, and the method comprises the steps: connecting a traffic monitoring camera, and obtaining traffic video data through the traffic monitoring camera; extracting traffic frame images in the traffic video data according to the frame rate of the traffic monitoring camera and a preset frame interval; converting the traffic frame image into a traffic RGB image, and inputting the traffic RGB image into a trained RSAN model; and calculating and outputting a traffic condition result of the current traffic RGB image. The technical effects of the invention are that the identification precision and the response speed of the system are improved, and the urban traffic congestion condition is efficiently and accurately identified.

    本申请涉及一种交通拥堵监测方法、装置、设备及存储介质,应用在深度学习领域,包括对接交通监控摄像头,并通过所述交通监控摄像头获取交通视频数据;根据所述交通监控摄像头的帧率和预设的帧间隔提取所述交通视频数据中的交通帧图像;将所述交通帧图像转换为交通RGB图像,并将所述交通RGB图像输入至训练完成的RSAN模型;计算并输出当前交通RGB图像的交通状况结果。本申请具有的技术效果是:提高系统的识别精度和响应速度,高效准确地识别城市交通拥堵状况。


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    Title :

    Traffic jam monitoring method and device, equipment and storage medium


    Additional title:

    交通拥堵监测方法、装置、设备及存储介质


    Contributors:
    ZHANG YEWEI (author) / ZHONG HONGWEI (author) / MAO CHENJIE (author) / ZHANG HONG (author)

    Publication date :

    2025-01-24


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06V / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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