SR-LMamba:用于复杂场景的车道检测模型,将曲线变换与Mamba架构集成在一起
Mingliang Chen1, Qinhao Jia1, Jing Yang1
1School of Computer Science and Technology, Xinjiang University, Urumqi, China.
PloS one
|October 31, 2025
概括
通过整合语义和几何特征,SR-LMamba 通过使用基于 Mamba 的新骨干来增强车道检测. 这种框架在具有挑战性的驾驶条件下实现了最先进的准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 自动驾驶自动驾驶的自动驾驶
背景情况:
- 由于语义和几何特征的整合不佳,现有的车道检测模型缺乏稳定性和准确性.
- 现实世界的驾驶环境对可靠的车道标记识别提出了复杂的挑战.
研究的目的:
- 提出SR-LMamba,一个新的车道检测框架,解决当前模型的局限性.
- 提高车道检测系统的准确性,稳定性和效率.
主要方法:
- 开发了SR-LMamba,这是一个利用Sketch-and-Refine范式的框架.
- 推出了LMamba,一个轻量级的三级骨干,结合了曲线变换和Mamba架构.
- 整合了交叉车道关联模块 (CLAM),以增强特征交互和曲线拟合的多项式回归.
- 设计了专门的角度丧失和交叉注意力丧失功能.
主要成果:
- SR-LMamba取得了显著的F1得分,达到80.04%.
- 该车型在四个具有挑战性的驾驶场景中表现出卓越的稳健性.
- 性能优于具有可比参数大小的现有最先进模型.
结论:
- 对于车道检测,SR-LMamba提供了一个强大而准确的解决方案.
- 拟议的架构有效地将高层次的语义理解与低层次的几何特征集成在一起.
- 公开发布的代码和模型有助于进一步的研究和开发.
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