RGB-thermal (RGB-T) semantic segmentation enables intelligent vehicles to understand environments while operating in urban scenes. However, the research encounters two main challenges: 1) scarcity of training samples under snowy conditions and 2) challenge in applying the model in practice. To address the first challenge, we proposed a publicly accessible RGB-T semantic segmentation dataset in snowy urban scenes (SUS dataset). The SUS dataset comprises 1035 pairs of precisely registered RGB-T images, and provides pixel-level semantic annotations for five categories for all images. To tackle the second challenge, we introduced MCNet-S ${}^{\ast }$ , a novel semantic segmentation model that leverages knowledge distillation (KD). The KD structure consists of an RGB-T teacher model, named MCNet-T, and an RGB student model, named MCNet-S. Within MCNet-T, we proposed a cross-modal dual association (CDA) module to enhance utilization of RGB-T information in snowy urban scenes. Within MCNet-S, a depth-wise separable pyramid (DSP) module was proposed to improve the efficiency of RGB information utilization and align the feature dimensions with those of MCNet-T. Between MCNet-S and MCNet-T, memory-based contrastive learning distillation (MCLD) was proposed to transfer the prior thermal knowledge, improving the segmentation accuracy of MCNet-S and obtaining optimized MCNet-S $\ast $ . Extensive experiments on the SUS and MFNet datasets show that the proposed models outperform state-of-the-art models. The SUS dataset and codes are available at https://github.com/xiaodonguo/SUS_dataset.


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    Titel :

    Transferring Prior Thermal Knowledge for Snowy Urban Scene Semantic Segmentation


    Beteiligte:
    Guo, Xiaodong (Autor:in) / Liu, Tong (Autor:in) / Mou, Yefeng (Autor:in) / Chai, Siyuan (Autor:in) / Ren, Bohan (Autor:in) / Wang, Yijin (Autor:in) / Shi, Wei (Autor:in) / Liu, Siyuan (Autor:in) / Zhou, Wujie (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    2935463 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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