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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    Title :

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


    Contributors:
    Lian, Zheng (author) / Nie, Yiming (author) / Dai, Bin (author) / Xu, Xiaoyu (author)


    Publication date :

    2022-10-08


    Size :

    2952239 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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