Semantic segmentation is a pivotal research area in the advancement of autonomous driving, with a particular focus on addressing adverse weather conditions such as night, rain, and fog. However, a challenging problem that remains is effectively segmenting environments that contain multiple adverse factors within the same scene. To tackle this issue, we present SCDF, an adaptive semantic segmentation approach. SCDF employs two separate image processing modules for denoising and enhancement of low-quality inputs. To enhance the denoising effect, we also optimized the loss function of its defogging module. Furthermore, we proposed a novel weight adaptation module, which enables SCDF to switch between different driving scenarios. To evaluate the effectiveness of SCDF, we conduct experiments on the ACDC and Rainy & Foggy cityscapes datasets, respectively achieving a higher mean Intersection over Union (mIoU) score of 61.4%(ACDC) and 79.1%(Rainy & Foggy Cityscapes). This promising results demonstrate the potential of SCDF in significantly improving the performance of autonomous driving systems in complex environments with multiple adverse factors.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SCDF: Seeing Clearly Through Dark and Fog, an Adaptive Semantic Segmentation Scheme for Autonomous Vehicle


    Beteiligte:
    Ying, Zuobin (Autor:in) / Lin, Zhengcheng (Autor:in) / Li, Zhenyu (Autor:in) / Huang, Xiaochun (Autor:in) / Ding, Weiping (Autor:in)


    Erscheinungsdatum :

    01.07.2025


    Format / Umfang :

    3629286 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Seeing Clearly Through Snowplow Windshields

    British Library Online Contents | 2006


    Seeing clearly

    Online Contents | 2002



    Motor vehicle front view mirror capable of seeing roads under strong light clearly

    PEI HUANLU / PEI LINFENG | Europäisches Patentamt | 2022

    Freier Zugriff