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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    Title :

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


    Contributors:
    Zhou, Fei (author) / Guo, Dudu (author) / Wang, Yang (author) / Zhao, Chenao (author)


    Publication date :

    2023-09-22


    Size :

    1724150 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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