Lane2Seq: Towards Unified Lane Detection via Sequence Generation
<|reference_start|>Lane2Seq: Towards Unified Lane Detection via Sequence Generation: In this paper, we present a novel sequence generation-based framework for lane detection, called Lane2Seq. It unifies various lane detection formats by casting lane detection as a sequence generation task. This is different from previous lane detection methods, which depend on well-designed task-specific head networks and corresponding loss functions. Lane2Seq only adopts a plain transformer-based encoder-decoder architecture with a simple cross-entropy loss. Additionally, we propose a new multi-format model tuning based on reinforcement learning to incorporate the task-specific knowledge into Lane2Seq. Experimental results demonstrate that such a simple sequence generation paradigm not only unifies lane detection but also achieves competitive performance on benchmarks. For example, Lane2Seq gets 97.95\% and 97.42\% F1 score on Tusimple and LLAMAS datasets, establishing a new state-of-the-art result for two benchmarks.<|reference_end|>
@article{zhou2024lane2seq:,
    title={Lane2Seq: Towards Unified Lane Detection via Sequence Generation},
    author={Kunyang Zhou},
    journal={arXiv preprint arXiv:2402.17172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17172},
    primaryClass={cs.CV}
}