一种基于行和变压器结构的快速准确车道检测方法
Yuxuan Chai1, Shixian Wang1,2, Zhijia Zhang1,2
1School of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究提高了使用变压器模型和特征对齐的金字塔网络的高级驾驶员辅助系统 (ADAS) 的车道检测. 改进的方法实现了实时性能和高精度,即使在复杂的驾驶场景.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 车道检测对于高级驾驶辅助系统 (ADAS) 至关重要,使车辆能够识别车道标记并确定其位置.
- 现有的深度学习车道检测方法面临着在有限的硬件上实时处理和在复杂环境中的准确性方面的挑战.
研究的目的:
- 增强基于行的车道检测方法,以提高复杂场景中的速度和准确性.
- 解决目前在自动驾驶系统中的深度学习方法的局限性.
主要方法:
- 利用变压器编码器解码器结构,在复杂的环境中增强全球特征提取和车道线路检测.
- 将特征对齐的金字塔网络 (FaPN) 作为一个辅助分支.
- 引入了一个新的结构损失与预期损失来完善检测准确性.
主要成果:
- 在RTX3080.0上实现了每秒129 (FPS) 的快速预测速度,单次预测时间为15.72ms.
- 在Tusimple数据集上获得了96.16%的准确性,比基线方法提高了3.32%.
- 证明了值得称赞的准确性和实时性能,适用于车载硬件.
结论:
- 改进的车道检测方法有效地解决了复杂驾驶场景中的速度和准确性挑战.
- 变压器和FaPN结构的集成,以及新的损失功能,显著提高了ADAS车道检测能力.
- 拟议的方法提供了一个可行的解决方案,用于实时,准确的车道检测,对于安全的自动驾驶至关重要.
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