This study focuses on the intelligence of urban traffic management, aiming to solve the increasingly severe urban traffic congestion and safety problems by integrating computer vision, deep learning technology, and Internet of Things technology. Based on the Faster R-CNN algorithm, a multifunctional integrated real-time traffic monitoring system was developed to achieve core functions such as traffic flow monitoring, pedestrian and non motorized vehicle detection, illegal behavior recognition, traffic sign recognition, and traffic accident detection. The system uses Raspberry Pi 4B as the main control chip, transmits data through Bluetooth module, and utilizes FREETROS operating system to achieve coordinated operation of tasks. The experimental results show that the system can recognize traffic targets with high accuracy (mAP50 is 56.3 %) and issue timely alarm information when a traffic accident is detected, demonstrating the effectiveness and reliability of the system in improving traffic management efficiency and ensuring traffic safety. This study provides new solutions and technical support for building a traffic management system for smart cities.


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    Title :

    Design of Multifunctional Integrated Real-Time Traffic Supervision System


    Contributors:
    Liu, Boyang (author)


    Publication date :

    2025-01-17


    Size :

    1620625 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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