As traffic systems grow in complexity and vehicle numbers increase, traditional management methods struggle to meet real-time demands. Intelligent Transportation Systems (ITS) offer a solution by integrating information and communication technologies for efficient traffic flow monitoring. This paper presents an advanced vehicle classification model, based on the InceptionV3 architecture and enhanced by a channel attention mechanism, to address challenges in fine-grained vehicle recognition under varying conditions such as diverse lighting, multi-angle perspectives, and occlusions. Tested on a comprehensive vehicle dataset, the model achieved 97% classification accuracy, significantly surpassing baseline models. Key contributions include a refined vehicle classification model for ITS, enhanced strategies for handling occlusions and multi-angle scenarios, and robust experimental validation across classification metrics, providing a solid foundation for ITS-driven traffic optimization and safety management.


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    Title :

    Adaptive Vehicle Recognition: Leveraging Channel Attention in InceptionV3 for Traffic Optimization


    Contributors:
    Yang, Yuyao (author)


    Publication date :

    2024-11-22


    Size :

    1497121 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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