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

本文提出了一种基于扩散模型的新型车道线检测方法DiffusionLane，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）添加高斯噪声得到带噪车道锚点，模型通过渐进式优化这些噪声锚点来还原目标车道。其次，针对噪声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生成高质量车道锚点。为进一步增强编码器特征表征，在训练阶段引入辅助头模块，采用可学习车道锚点来强化对编码器的监督信号。在四个基准数据集（Carlane、Tusimple、CULane和LLAMAS）上的实验表明，相较于现有最优方法，DiffusionLane具有强泛化能力和优异检测性能。例如，采用ResNet18的DiffusionLane在域适应数据集Carlane上以至少1%的准确率优势超越现有方法；采用MobileNetV4的模型在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.