Vehicle detection is the important basis for smart traffic. In order to further improve the performance of vehicle detection in freeway scenes, an improved YOLOv7 network is proposed in this paper. Firstly, vehicle dataset is created via cameras mounted on freeway. Secondly, Improved data augmentation method is applied to simulated the special environment interference. Then, SimAM attention mechanism module is inserted into YOLOv7, and the optimal adding position is obtained by discussing the performance of different attention composition. Finally, we provide experimental results to verify the effectiveness of the improvement. The results show that method proposed in this paper increase the mAP by 11% than original YOLOv7 on our dataset.


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    Title :

    Freeway vehicle detection on improved YOLOv7


    Contributors:
    Shen, Linlin (editor) / Zhong, Guoqiang (editor) / Gong, Shaojie (author) / Mao, Kun (author) / Yuan, Haifeng (author) / Zheng, Yongjie (author) / Zhang, Shu (author) / Zhou, Qiang (author) / Zou, Lu (author) / Huang, Zhen (author)

    Conference:

    Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023) ; 2023 ; Hangzhou, China


    Published in:

    Proc. SPIE ; 12754


    Publication date :

    2023-08-01





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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