On-road object detection is an important part of driverless technology. The on-road object detection task requires both detection speed and accuracy. We propose an improved RepVGG-based anchor-free real-time object detection algorithm to meet these requirements. The RepVggmodule is improved by a reparameterization method, and an adaptive Fusion-Distribution Feature Pyramid Network(FDFPN) structure is proposed, based on which an anchor-free object detection head with fewer hyperparameters is constructed to balance accuracy and speed. Experiments on KITTI dataset show that the accuracy of this method can reach 80.01%, and the inference latency is only 5.9ms in deployment mode.


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    Titel :

    Improved RepVGG-based Anchor-free Algorithm for On-road Object Detection


    Beteiligte:
    Lian, Zheng (Autor:in) / Nie, Yiming (Autor:in) / Dai, Bin (Autor:in) / Xu, Xiaoyu (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    2952239 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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