Motorcycle accidents contribute substantially to morbidity and mortality. Motorcyclists are injured when their motorcycles fall. The impact of a motorcycle fall can cause brain injuries and many bone fractures; therefore, it is beneficial if we can identify fallen motorcycles early. The study proposed a method to identify motorcycle falls using You Only Look Once (YOLO) and a machine learning classifier. The study used YOLO to detect a motorcycle. Then we studied the performance of three machine learning classifiers to classify the detectable motorcycles into two categories: those that do not fall over and those that do. MobileNet, ResNet50, and VGG16 classifiers could achieve accuracies of 98.75%, 97.50%, and 100%, respectively, for motorcycle fall classification.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Hybrid YOLO-VGG16 for Fallen Motorcycle Detection


    Contributors:


    Publication date :

    2025-03-05


    Size :

    1143524 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    HYBRID MOTORCYCLE

    AN JEONG KI | European Patent Office | 2020

    Free access

    Hybrid motorcycle

    RHA JEONG DUK / HONG CHAN YOUNG / KIM YEON YI | European Patent Office | 2021

    Free access

    HYBRID MOTORCYCLE

    AN JEONG KI | European Patent Office | 2020

    Free access

    HYBRID MOTORCYCLE

    AN JEONG KI | European Patent Office | 2020

    Free access

    Hybrid-driven motorcycle and hybrid kit of motorcycle

    MORFINO LUCA | European Patent Office | 2022

    Free access