In the context of smart cities, transportation stands out as a critical aspect, showcasing innovative solutions to tackle congestion, emissions, and overall mobility challenges. From intelligent traffic management systems to the proliferation of shared mobility services, these cities prioritize efficiency and sustainability. Moreover, they foster the development of electric and autonomous vehicles, enhancing safety and reducing carbon footprints. Intelligent transportation systems are crucial in addressing transportation challenges within smart cities. These systems harness cutting-edge technology to optimize efficiency, safety, and sustainability by enabling dynamic traffic management and promoting multimodal connectivity. Object detection technology further enhances traffic management by providing real-time data for proactive intervention and optimization of traffic patterns. Object detection algorithms revolutionize traffic management by accurately identifying and tracking vehicles in real-time. Additionally, unmanned aerial vehicles (UAVs) serve as powerful tools for aerial traffic monitoring, providing valuable real-time imagery for object detection algorithms. This study focuses on enhancing ITS within smart cities through the integration of UAV-based aerial monitoring and the SSD detection algorithm for vehicle detection. By compiling a large dataset using vehicle images acquired through UAV aerial monitoring, the study demonstrates robust and real-time vehicle detection capabilities. The integration of UAV-based aerial monitoring with SSD vehicle detection holds immense potential for improving overall transportation within cities, contributing to safer, more efficient, and sustainable urban environments.


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    Title :

    Integrating UAV-Based Aerial Monitoring and SSD for Enhanced Traffic Management in Smart Cities


    Contributors:


    Publication date :

    2024-05-02


    Size :

    1425747 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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