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展现了出色的性能，但大多数方法仍需要后处理，且鲁棒性不足。因此，端到端检测器（如DEtection TRansformer，DETR）被引入车道检测领域。然而，DETR中的一对一标签分配因标签语义冲突而降低了训练效率。此外，DETR中的位置查询无法提供显式的位置先验，导致优化困难。本文提出了一对多Transformer（O2SFormer）。首先，我们提出一对多标签分配方法，该方法结合一对一和一对多标签分配，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困难的问题，我们进一步提出逐层软标签，在不同解码器层中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探索位置先验。实验结果表明，O2SFormer显著加速了DETR的收敛速度，并在CULane数据集上优于基于Transformer和CNN的检测器。代码将发布于https://github.com/zkyseu/O2SFormer。