结合低光场景增强,实现快速准确的车道检测
Changshuo Ke1, Zhijie Xu1, Jianqin Zhang2
1School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种用于自动驾驶的快车道检测的新方法,在低光条件下显著提高了准确性. 这种方法增强了图像,并改进了可靠导航的功能.
科学领域:
- 计算机视觉 计算机视觉
- 自主驾驶系统 自主驾驶系统
- 人工智能的人工智能
背景情况:
- 车道检测对于自动驾驶汽车的导航至关重要.
- 挑战包括低光,遮蔽和模糊的车道线,使特征识别复杂化.
- 现有的方法在可见度差的情况下扎.
研究的目的:
- 开发一种改进的自动驾驶车道检测方法,特别针对低光条件.
- 为了提高车道检测系统的稳定性和准确性.
- 将低光增强与先进的功能改进技术相结合.
主要方法:
- 拟议的低光增强快车道检测 (LLFLD) 方法.
- 使用自动低光场景增强网络 (ALLE) 进行图像预处理.
- 集成的对称特征翻转模块 (SFFM) 和通道融合自动注意机制 (CFSAT) 用于特征改进.
- 设计了一个新的结构损失函数,结合了车道几何先验.
主要成果:
- 与最先进的方法相比,LLFLD在CULane数据集上的表现优越.
- 在白天和晚上都观察到显著的改善,特别是在具有挑战性的低光场景中.
- 综合方法有效地提高了对比度,降低了噪音,并改善了特征区分.
结论:
- 拟议的LLFLD方法提供了一个强大的解决方案,用于在不利的照明下检测车道.
- 低光增强和高级功能处理模块的组合是有效的.
- 这项工作有助于在各种条件下提高自动驾驶系统的安全性和可靠性.
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