LDTR: Transformer-based Lane Detection with Anchor-chain Representation
<|reference_start|>LDTR: Transformer-based Lane Detection with Anchor-chain Representation: Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving. Moreover, current lane representations require complex post-processing and struggle with specific instances. Inspired by the DETR architecture, we propose LDTR, a transformer-based model to address these issues. Lanes are modeled with a novel anchor-chain, regarding a lane as a whole from the beginning, which enables LDTR to handle special lanes inherently. To enhance lane instance perception, LDTR incorporates a novel multi-referenced deformable attention module to distribute attention around the object. Additionally, LDTR incorporates two line IoU algorithms to improve convergence efficiency and employs a Gaussian heatmap auxiliary branch to enhance model representation capability during training. To evaluate lane detection models, we rely on Frechet distance, parameterized F1-score, and additional synthetic metrics. Experimental results demonstrate that LDTR achieves state-of-the-art performance on well-known datasets.<|reference_end|>
@article{yang2024ldtr:,
    title={LDTR: Transformer-based Lane Detection with Anchor-chain Representation},
    author={Zhongyu Yang, Chen Shen, Wei Shao, Tengfei Xing, Runbo Hu, Pengfei Xu,
  Hua Chai, Ruini Xue},
    journal={arXiv preprint arXiv:2403.14354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14354},
    primaryClass={cs.CV}
}