Although lane detection methods have shown impressive performance in real-world scenarios, most of methods require post-processing which is not robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR) have been introduced in lane detection. However, one-to-one label assignment in DETR can degrade the training efficiency due to label semantic conflicts. Besides, positional query in DETR is unable to provide explicit positional prior, making it difficult to be optimized. In this paper, we present the One-to-Several Transformer(O2SFormer). We first propose the one-to-several label assignment, which combines one-to-one and one-to-many label assignments to improve the training efficiency while keeping end-to-end detection. To overcome the difficulty in optimizing one-to-one assignment. We further propose the layer-wise soft label which adjusts the positive weight of positive lane anchors across different decoder layers. Finally, we design the dynamic anchor-based positional query to explore positional prior by incorporating lane anchors into positional query. Experimental results show that O2SFormer significantly speeds up the convergence of DETR and outperforms Transformer-based and CNN-based detectors on the CULane dataset. Code will be available at https://github.com/zkyseu/O2SFormer.

翻译：尽管车道检测方法在真实场景中已展现出卓越性能，但多数方法仍需依赖鲁棒性不足的后处理步骤。为此，DEtection TRansformer（DETR）等端到端检测器被引入车道检测领域。然而，DETR中的一对一标签分配会因标签语义冲突而降低训练效率。此外，DETR中的位置查询无法提供显式的位置先验，导致其难以优化。本文提出了一对多Transformer（O2SFormer）。我们首先提出了一对多标签分配策略，该策略结合一对一与一对多标签分配，在保持端到端检测的同时提升训练效率。为克服一对一分配优化难题，我们进一步提出分层软标签机制，该机制在不同解码器层调整正车道锚点的正权重。最后，我们设计了基于动态锚点的位置查询，通过将车道锚点融入位置查询来挖掘位置先验。实验结果表明，O2SFormer显著加速了DETR的收敛速度，并在CULane数据集上超越了基于Transformer和CNN的检测器。代码地址：https://github.com/zkyseu/O2SFormer。