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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Yang, Yuyao (Autor:in)


    Erscheinungsdatum :

    22.11.2024


    Format / Umfang :

    1497121 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Revolutionizing Alzheimer's Disease Prediction using InceptionV3 in Deep Learning

    Jansi, R. / Gowtham, Naraganti / Ramachandran, Siva et al. | IEEE | 2023


    Autism Detection using InceptionV3 and Security using Blockchain

    Annie Silviya, S H / Seetha, R / Raja, S et al. | IEEE | 2024



    Leveraging traffic patterns to understand traffic rules

    OMARI SAMMY / QURESHI SAMEER | Europäisches Patentamt | 2025

    Freier Zugriff

    Leveraging Neo4j and deep learning for traffic congestion simulation & optimization

    Singh, Shyam Pratap / Khan, Arshad Ali / Souissi, Riad et al. | ArXiv | 2023

    Freier Zugriff