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[bibtex]@InProceedings{Zhou_2024_CVPR, author = {Zhou, Kunyang}, title = {Lane2Seq: Towards Unified Lane Detection via Sequence Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {16944-16953} }
Lane2Seq: Towards Unified Lane Detection via Sequence Generation
Abstract
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.
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