In the fusion-based vehicle positioning, the lane detection is applied to provide the relative position of ego-vehicle and lanes, which is critical to subsequent tasks including trajectory planning and behaviour decision. However, the performance of current vision-based lane detectors drop significantly when facing adversarial visual conditions, e.g., low-illumination conditions like night scenarios. Images captured in this scenario often suffer from low contrast, low brightness and noise, which is challenging for detectors to extract correct information. To facilitate the lane detection in low-illumination conditions, this paper presents a novel framework which integrates image feature enhancement with lane detection. The framework consists of two modules: an image enhancement module to enhance and extract information from low visibility images, and a detection module to regress the lane parameters. Both modules are optimized by loss collaboration. Experiments on popular lane detection benchmark showed that the proposed method achieved significant and consistent improvements. With our framework implemented, the performance of two SOTA detectors increases by 1.32% and 0.67% in low-illumination conditions.


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    Titel :

    Lane Detection under Low-illumination Condition by Enhanced Feature Learning


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Lu, Chengyu (Autor:in) / Tian, Wei (Autor:in) / Zhao, Wenbo (Autor:in) / Yu, Xianwang (Autor:in)

    Kongress:

    SAE 2022 Intelligent and Connected Vehicles Symposium ; 2022



    Erscheinungsdatum :

    2022-12-22




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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