Vehicle monitoring using cameras is an important task for highway management. At present, most vehicle detection algorithms are applied in daytime. However, compared with the daytime, the road environment at night is darker and the vehicle body is not obvious, the existing algorithm is difficult to meet the vehicle detection requirements in the night scene. Due to the complexity of night scenes, video images bluring and excessive noise, vehicle detection at night poses a great challenge. Aiming at the above problems, this paper proposes a nighttime vehicle detection framework based on improved Fasterr R-CNN. Firstly, the deep residual network is used to extract features autonomously, and the spatial attention mechanism is integrated to make the network pay more attention to the road region rather than the background region. Secondly, balanced feature module is introduced to make full use of the extracted visual features. Finally, Soft- NMS replaces NMS to reduce the number of missed-detection vehicles. Experimental results show that the AP value of the improved Faster R-CNN model is 4.18% higher than that of baseline Faster R-CNN model.


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    Titel :

    Nighttime vehicle detection on highway based on improved faster R-CNN model


    Beteiligte:
    Guo, Yiqing (Autor:in) / Zhao, Min (Autor:in)

    Kongress:

    2nd International Conference on Computer Vision, Image, and Deep Learning ; 2021 ; Liuzhou,China


    Erschienen in:

    Proc. SPIE ; 11911


    Erscheinungsdatum :

    05.10.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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