  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-many and one-to-one label assignment to
solve label semantic conflicts 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 dynamically adjusts the positive weight of positive
lane anchors in 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 with
ResNet50 backbone achieves 77.83% F1 score on CULane dataset, outperforming
existing Transformer-based and CNN-based detectors. Futhermore, O2SFormer
converges 12.5x faster than DETR for the ResNet18 backbone.
