DiffusionLane：基于扩散模型的车道线检测方法

DiffusionLane: Diffusion Model for Lane Detection

October 25, 2025

作者: Kunyang Zhou, Yeqin Shao

cs.AI

摘要

本文提出一种基于扩散模型的车道线检测新方法——DiffusionLane，该方法将车道线检测任务建模为车道参数空间中的去噪扩散过程。首先，我们对真实车道线参数（起点坐标与角度）添加高斯噪声生成含噪车道锚点，模型通过渐进式优化这些含噪锚点来还原目标车道。其次，针对含噪锚点导致的编码器特征表征能力弱化问题，我们提出混合解码策略：设计融合全局与局部解码器的混合扩散解码器以提升锚点质量。此外，在训练阶段引入辅助学习头，采用可学习车道锚点增强对编码器的监督信号。在Carlane、Tusimple、CULane和LLAMAS四个基准数据集上的实验表明，DiffusionLane相比现有最优方法具有更强的泛化能力和检测性能。例如，采用ResNet18的DiffusionLane在域适应数据集Carlane上准确率至少超越现有方法1%；采用MobileNetV4时在CULane上取得81.32%的F1分数，ResNet34版本在Tusimple上达到96.89%准确率，而ResNet101版本在LLAMAS上获得97.59%的F1分数。代码已开源于https://github.com/zkyntu/UnLanedet。

English

In this paper, we present a novel diffusion-based model for lane detection, called DiffusionLane, which treats the lane detection task as a denoising diffusion process in the parameter space of the lane. Firstly, we add the Gaussian noise to the parameters (the starting point and the angle) of ground truth lanes to obtain noisy lane anchors, and the model learns to refine the noisy lane anchors in a progressive way to obtain the target lanes. Secondly, we propose a hybrid decoding strategy to address the poor feature representation of the encoder, resulting from the noisy lane anchors. Specifically, we design a hybrid diffusion decoder to combine global-level and local-level decoders for high-quality lane anchors. Then, to improve the feature representation of the encoder, we employ an auxiliary head in the training stage to adopt the learnable lane anchors for enriching the supervision on the encoder. Experimental results on four benchmarks, Carlane, Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong generalization ability and promising detection performance compared to the previous state-of-the-art methods. For example, DiffusionLane with ResNet18 surpasses the existing methods by at least 1\% accuracy on the domain adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets 81.32\% F1 score on CULane, 96.89\% accuracy on Tusimple with ResNet34, and 97.59\% F1 score on LLAMAS with ResNet101. Code will be available at https://github.com/zkyntu/UnLanedet.