HWLane: HW-Transformer for Lane Detection

Engineering notes

Extensive experiments on BDD100K, TuSimple, CULane, and VIL100 datasets demonstrate that our method outperforms the state-of-the-art segmentation-based lane detection methods. We also apply the proposed SAKD to DeiT-tiny, and it achieves 1.51 Top-1 accuracy improvement on ImageNet-1K dataset.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Lane detection is one of the most fundamental tasks in autonomous driving perception, but it still faces many challenges in some special driving scenarios. For example, in dazzling light, crowded roads, etc., lane detection is very dependent on surrounding visual cues. Previous segmentation-based lane detection methods have not paid enough attention to the surrounding visual range, resulting in poor performance. In this paper, we design a novel lane detection network namely HW-Transformer, which is based on row and column multi-head self-attention. It restricts the attention only to their respective rows and columns, and transfers information across rows and columns by intersection features. In this way, the attention to the visual range around the lane is greatly expanded, and the communication of global information can be achieved through intersecting features. In addition, we further propose a self-attention knowledge distillation (SAKD) method for the Transformer model, where higher-level attention guides lower-level attention to learn. SAKD not only helps to improve the performance of lane detection, but also has universality in better learning semantic features from general images. Extensive experiments on BDD100K, TuSimple, CULane, and VIL100 datasets demonstrate that our method outperforms the state-of-the-art segmentation-based lane detection methods. We also apply the proposed SAKD to DeiT-tiny, and it achieves 1.51 Top-1 accuracy improvement on ImageNet-1K dataset. Our code will be available at https://github.com/Cuibaby/HWLane.

Links and sources

• Official / arXiv page

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