As society progresses, urban vehicle numbers have surged, increasing pressure on traffic systems. Intelligent transportation has been prioritized for traffic management, with vehicle recognition technology playing a crucial role as a primary data source. While foundational research on vehicle detection and feature recognition is mature, practical applications are still limited. This paper proposes a YB-CNN algorithm that merges vehicle detection and feature recognition algorithms. It fully utilizes detection results for accurate feature identification. Experiments indicate significant performance improvements, showcasing practical value and broad potential applications. The YB-CNN-based vehicle attribute recognition technology is widely used in urban traffic and aviation. For instance, it can monitor and identify airport ground vehicles like baggage carts, fuel trucks, and cleaning vehicles, enhancing safety and efficiency. Additionally, it aids in airport parking management, improving space utilization, reducing congestion, and providing a better parking experience.


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    Title :

    Integration Research of Vehicle Attribute Recognition Algorithm Based on YB-CNN


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Zou, Jiaqi (editor) / Sun, Gang (editor) / Wang, Yue (editor) / Xu, Lexi (editor) / Li, Jianbin (author) / Zhang, Yang (author) / Fang, Rui (author) / Wu, Bin (author) / Xiao, Yongqiang (author) / Zhang, Xiaocong (author)

    Conference:

    International Conference On Signal And Information Processing, Networking And Computers ; 2024 ; China September 10, 2024 - September 13, 2024



    Publication date :

    2025-05-16


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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