In response to the current challenge of simultaneously balancing detection speed and accuracy in vehicle detection, this study presents a research on an improved YOLOv5s-based vehicle detection method. By enhancing the existing model, we optimize the loss function of the training model and incorporate an attention mechanism. Additionally, a new dataset is established and trained. While ensuring swift vehicle recognition, we further enhance detection precision. Furthermore, a vehicle tracking module is introduced to the output of the vehicle detection, enabling prediction of the driving behavior of preceding vehicles. Through this research methodology, real-time vehicle detection and tracking are successfully achieved, yielding significant results when applied to vehicle-following systems. This approach not only enables rapid and accurate vehicle identification but also provides real-time monitoring of the driving conditions of vehicles ahead, issuing timely warnings to aid drivers in making necessary adjustments and thereby reducing the probability of rear-end accidents.


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    Title :

    Research on Vehicle Detection Method Based on Improved YOLOv5s


    Contributors:
    Ma, Liangliang (author) / Zhong, Runlu (author) / Shi, Xiaohong (author) / Yang, Peng (author)


    Publication date :

    2024-12-20


    Size :

    1262424 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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