Angle-Aware Rectangle Anchors for Lane Detection: Addressing Geometric Misalignments in Anchor-Based Representations

Lane detection is a fundamental task in computer vision with broad applications in autonomous driving. Recent advances have focused on line-anchor-based representations for their efficiency and high accuracy. However, most existing methods adopt anchors that are continuous in point sampling yet discrete in width and fixed in direction. This leads to geometric misalignments with real-world lane lines, which exhibit continuous width representation and smoothly varying directions. These misalignments cause Symmetric Point Ambiguity and Magnified Localization Errors, ultimately degrading both evaluation reliability and detection accuracy. To address these issues, we propose Angle-Aware Rectangle Anchors (ARA), a novel representation with continuous width and adaptive directional alignment, that effectively captures the geometric variation of lane lines. In addition, we propose the Three-phase Angle-Thresholded Line-Area Transition (TALAT) Loss, which dynamically switches between loss formulations based on angular thresholds and overlap quality, enabling a smooth transition from coarse to fine supervision. Extensive experiments on multiple standard benchmarks (TuSimple, CULane, CurveLanes, and LLAMAS) demonstrate that our approach achieves competitive or state-of-the-art (SOTA) performance. Notably, ARA exhibits superior robustness in challenging scenarios, while maintaining real-time inference speed. Our code and models are publicly available at https://github.com/changehome717/ARA-Lane-Detection.