OTVLD-Net:用于车道检测的万维动态卷积变压器网络.
Yunhao Wu1, Ziyao Zhang2,3, Haifeng Chen1
1College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
一个新的车道检测网络,OTVLD-Net,通过结合独特的车道特征,提高了在具有挑战性的道路条件中的适应性. 这种深度学习模型实现了高级性能和实时处理,以提高自动驾驶的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 自主驾驶系统 自主驾驶系统
背景情况:
- 深度学习已经推进了车道检测,但由于对独特车道特征的考虑有限,当前的模型难以应对具有挑战性的场景.
- 现有的方法在复杂的车道拓和极端道路条件下遇到困难和局限性.
研究的目的:
- 提出一个新的车道检测网络,OTVLD-Net,可以提高在极端道路条件下的适应性,并处理复杂的车道拓.
- 为了增强情境特征的提取和汇总车道对称性,以实现更强大的车道检测.
主要方法:
- 使用全维卷积变压器开发了OTVLD-Net,结合了具有动态卷积,功能翻转融合和非局部网络层的ODVT-Net.
- 集成了一个基于变压器的功能重量生成机制,交叉注意力和一个失踪点检测模块.
- 采用联合加权损失功能进行协调培训,以促进泛化.
主要成果:
- 在OpenLane和CurveLanes数据集上,OTVLD-Net实现了先进的检测性能,与排名第二的模型相比,在OpenLane上F1得分高出6.4%.
- 在具有挑战性的场景中表现出8.9%的平均绩效改善.
- 使用ResNet-18.2实现了103FPS和14.2GFlops的实时性能.
结论:
- OTVLD-Net显著提高了车道检测的准确性和适应性,特别是在具有挑战性的道路条件下.
- 该模型在高性能和自动驾驶实时处理能力之间提供了强大的平衡.
- 提出的方法有效地汇总了全球和当地特征,提高了车道检测的稳定性.
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