Lane detection plays an important role in the autonomous driving system and attracts widespread interest in recent years. Despite the advantages of conventional work, such as segmentation-based and point-based methods, these models either practically require a large sum of anchors or only leverage the connection message of discrete points in the lane. Different from this, curve-based methods enable one to naturally learn the holistic representation of lanes based on the geometric semantics of curves and conduct end-to-end optimization conveniently, thus serving a promising direction. However, the existing curve-based methods encounter draws in: 1) modeling some complicated curves mathematically. 2) Simultaneously capturing geometric semantics within and between lane curves. 3) The endpoints of lane lines are susceptible to occlusion, a factor that is crucial for curve modeling. To tackle these issues, we revisit the curve-based methods and propose a novel model Dynamic NURBS Network (DBNet). Specifically, we introduce the NURBS curve to model lanes, enabling to theoretically fit various complicated curves and guarantee the robustness of local and global optimization. Based on this, we introduce three key modules to address lane detection challenges. Firstly, we propose Local Dynamic-Interaction (LDI) that adaptively exploits the geometric message of local at the feature level. Secondly, a Curve Fitting Enhancement (CFE) module to enhance the feature of both ends of the lane instance. Lastly, a Global Association-Sharing (GAS) module captures the global features of lanes, which can together promote semantic association and tackle the occlusion problem inside and outside the lane. Moreover, the proposed method achieves a new state-of-the-art performance on three public benchmarks and especially achieves 77.98% of the F1 score on the curve scenario in the CULane dataset.


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    Title :

    DBNet: A Curve-Based Dynamic Association Framework for Lane Detection


    Contributors:
    Dai, Xinguang (author) / Xie, Jun (author) / Zhang, Guoxin (author) / Chang, Kenglun (author) / Chen, Feng (author) / Wang, Zhepeng (author) / Tang, Chunming (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    9627574 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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