Lane detection algorithms play a key role in Advanced Driver Assistance Systems (ADAS), which are however unable to achieve accurate lane recognition in low-light environments. This paper presents a novel deep network structure, namely LLSS-Net (low-light images semantic segmentation), to achieve accurate lane detection in low-light environments. The method integrates a convolutional neural network for low-light image enhancement and a semantic segmentation network for lane detection. The image quality is firstly improved by a low-light image enhancement network and lane features are then extracted using semantic segmentation. Fast lane clustering is finally performed by using the KD tree models. Cityscapes and Tusimple datasets are utilized to demonstrate the robustness of the proposed method. The experimental results show that the proposed method has an excellent performance for lane detection in low-light roads.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A novel deep learning network for accurate lane detection in low-light environments


    Beteiligte:
    Song, Shuang (Autor:in) / Chen, Wei (Autor:in) / Liu, Qianjie (Autor:in) / Hu, Huosheng (Autor:in) / Huang, Tengchao (Autor:in) / Zhu, Qingyuan (Autor:in)


    Erscheinungsdatum :

    2022-02-01


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Deep Learning Approach for Lane Detection

    Getahun, Tesfamchael / Karimoddini, Ali / Mudalige, Priyantha | IEEE | 2021


    Lane and Curve Detection using Deep Learning

    Singh, Shubham / Malik, Shruti / Nath, Rajiv Kumar | IEEE | 2021


    Deep Learning in Lane Marking Detection: A Survey

    Zhang, Youcheng / Lu, Zongqing / Zhang, Xuechen et al. | IEEE | 2022


    Deep Learning-Based Lane Marking Detection using A2-LMDet

    Lin, Chunmian / Li, Lin / Cai, Zhixing et al. | Transportation Research Record | 2020


    Deep-Learning-Based Anomaly Detection for Lane-Changing Decisions

    Wang, Sheng-Li / Lin, Chien / Boddupalli, Srivalli et al. | IEEE | 2022