In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multi-scale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation


    Contributors:
    Gao, Guangwei (author) / Xu, Guoan (author) / Yu, Yi (author) / Xie, Jin (author) / Yang, Jian (author) / Yue, Dong (author)

    Published in:

    Publication date :

    2022-12-01


    Size :

    9933824 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN

    Xu, Guoan / Li, Juncheng / Gao, Guangwei et al. | IEEE | 2023


    A Lightweight RGB-T Fusion Network for Practical Semantic Segmentation

    Zhang, Haoyuan / Li, Zifeng / Wu, Zhenyu et al. | IEEE | 2023



    MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes

    Lu, Mengxu / Chen, Zhenxue / Liu, Chengyun et al. | IEEE | 2022