论文标题
基于热图的消失点提升车道检测
Heatmap-based Vanishing Point boosts Lane Detection
论文作者
论文摘要
基于视觉的车道检测（LD）是自动驾驶技术的关键部分，这也是一个具有挑战性的问题。作为场景组成的重要约束之一，消失点（VP）可能为车道检测提供了有用的线索。在本文中，我们提出了一种新的多任务融合网络体系结构，用于高精度车道检测。首先，ERFNET被用作提取道路图像的层次特征的骨干。然后，使用图像分割检测到车道。最后，使用热图回归将车道检测的输出和主链提取的分层特征结合在一起。提出的融合策略是使用公共Culane数据集测试的。实验结果表明，我们方法的车道检测准确性优于最先进的方法（SOTA）方法。
Vision-based lane detection (LD) is a key part of autonomous driving technology, and it is also a challenging problem. As one of the important constraints of scene composition, vanishing point (VP) may provide a useful clue for lane detection. In this paper, we proposed a new multi-task fusion network architecture for high-precision lane detection. Firstly, the ERFNet was used as the backbone to extract the hierarchical features of the road image. Then, the lanes were detected using image segmentation. Finally, combining the output of lane detection and the hierarchical features extracted by the backbone, the lane VP was predicted using heatmap regression. The proposed fusion strategy was tested using the public CULane dataset. The experimental results suggest that the lane detection accuracy of our method outperforms those of state-of-the-art (SOTA) methods.
