Lane detection is one of the most important task in autonomous driving. While the semantic segmentation based method is widely explored and recognized in recent decade, some post-processing are required to estimate the exact location of the predicted lane markings and can be easily failed in complex scenarios. To tackle these limitations, this paper proposes a novel lane detection network named PCRLaneNet. Firstly, we use a fully convolutional network to predict the coordinates of lane marking points directly, which can better meet with the requirements of autonomous driving. Secondly, to take the fully advantage of the correlation of these lane marking points, a point feature fusion strategy is designed to fuse feature maps of the points on the same lane marking, which makes our method capable of handling challenging scenarios. Lastly, the robustness, accuracy and latency of the proposed method are extensively verified in two datasets (CULane and TuSimple).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    PCRLaneNet: Lane Marking Detection via Point Coordinate Regression


    Beteiligte:
    Wang, Pan (Autor:in) / Xue, Jianru (Autor:in) / Dou, Jian (Autor:in) / Wang, Di (Autor:in) / Zhao, Haibo (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    6465975 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    PCRLANENET: LANE MARKING DETECTION VIA POINT COORDINATE REGRESSION

    Wang, Pan / Xue, Jianru / Dou, Jian et al. | British Library Conference Proceedings | 2021


    LANE MARKING DETECTION SYSTEM AND LANE MARKING DETECTION METHOD

    TAKEMAE KASHU | Europäisches Patentamt | 2015

    Freier Zugriff

    Lane Marking Regression From Confidence Area Detection to Field Inference

    Lv, Hui / Liu, Chao / Zhao, Xiaowei et al. | IEEE | 2021


    LANE MARKING DETECTION SYSTEM

    OGASAWARA SHIGERU / UMEMI YUICHI / UNO AKINORI et al. | Europäisches Patentamt | 2018

    Freier Zugriff