Nowadays, semantic segmentation methods for systems in road scene have a great demand. Most existing methods focus on high accuracy with low inference speed. And some approaches emphasize on speed, significantly sacrificing model accuracy. To make a trade-off between accuracy and inference speed, we propose a real-time network for semantic segmentation titled Factorized and Regular Network (FRNet), which employs an asymmetric encoder-decoder architecture with Factorized and Regular (FR) blocks. Our method achieves 70.4% mIoU on the Cityscapes test set with 1 million parameters at a speed of 127 frames per second (FPS) on a single Titan Xp at a resolution of $512\times 1024$ . We evaluate FRNet on Cityscapes, Camvid, Kitti, and Gatech datasets to identify that our network stands out from other state-of-the-art networks.


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    Titel :

    FRNet: Factorized and Regular Blocks Network for Semantic Segmentation in Road Scene


    Beteiligte:
    Lu, Mengxu (Autor:in) / Chen, Zhenxue (Autor:in) / Wu, Q. M. Jonathan (Autor:in) / Wang, Nannan (Autor:in) / Rong, Xuewen (Autor:in) / Yan, Xinghe (Autor:in)


    Erscheinungsdatum :

    2022-04-01


    Format / Umfang :

    2306075 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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