Lane detection is to determine the precise location and shape of lanes on the road. Despite efforts made by current methods, it remains a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. Proposal-based methods detect lanes by distinguishing and regressing a collection of proposals in a streamlined top-down way, yet lack sufficient flexibility in lane representation. Keypoint-based methods, on the other hand, construct lanes flexibly from local descriptors, which typically entail complicated post-processing. In this paper, we present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods. The motivation is that local directions of lanes are semantically simple and clear. At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers. Then we can build a set of lane proposals accordingly with moderate accuracy. At the "Refine" stage, we further optimize these proposals via a novel Lane Segment Association Module (LSAM), which allows adaptive lane segment adjustment. Last but not least, we propose multi-level feature integration to enrich lane feature representations more efficiently. Based on the proposed "Sketch and Refine" paradigm, we propose a fast yet effective lane detector dubbed "SRLane". Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9\%. The source code is available at: https://github.com/passerer/SRLane.

翻译：车道检测旨在确定道路上车道的精确位置与形状。尽管现有方法已付出诸多努力，但由于真实场景的复杂性，这仍是一项具有挑战性的任务。无论是基于提议的方法还是基于关键点的方法，现有方法在高效描述车道方面均存在不足。基于提议的方法通过自上而下的简化方式区分并回归一组提议来实现检测，但缺乏足够的车道表示灵活性；而基于关键点的方法则通过局部描述符灵活构建车道，但通常需要复杂的后处理。本文提出一种“草图与细化”（Sketch-and-Refine）范式，融合了基于关键点方法与基于提议方法的优势。其动机在于：车道的局部方向在语义上简单明确。在“草图”阶段，可通过快速卷积层轻松估计关键点的局部方向，进而构建一组中等精度的车道提议。在“细化”阶段，我们进一步通过新颖的车道段关联模块（LSAM）优化这些提议，实现自适应的车道段调整。最后，我们提出多级特征融合以更高效地丰富车道特征表示。基于所提出的“草图与细化”范式，我们构建了快速且有效的车道检测器“SRLane”。实验表明，我们的SRLane在取得78.9%的F1分数时，能以278 FPS的快速速度运行。源代码发布于：https://github.com/passerer/SRLane。