To address the problem that it is difficult to balance the accuracy and speed of vehicle detection in current traffic monitoring scenarios, this paper proposes a lightweight vehicle detection algorithm based on YOLOv8. First, the detection speed and accuracy of the model are improved by replacing the Neck layer of the original model with slimneck; second, the SimAM attention mechanism is introduced to strengthen the key features of the vehicle and suppress the non-key features; and finally, the WIoU loss function is adopted to achieve lower regression error. The experimental results on the UA-DETRAC dataset show that, compared with the base model, the improved method in this paper improves 2.66% and 7.14% in the two indexes of mAP and FPS, which effectively improves the problem of lower vehicle detection accuracy in complex traffic scenarios and achieves faster detection speed.


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    Titel :

    Improved YOLOv8-based Vehicle Detection Method for Road Monitoring and Surveillance


    Beteiligte:
    Zhou, Fei (Autor:in) / Guo, Dudu (Autor:in) / Wang, Yang (Autor:in) / Zhao, Chenao (Autor:in)


    Erscheinungsdatum :

    22.09.2023


    Format / Umfang :

    1724150 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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