CARLANE：一个从仿真到多个真实世界域的无监督域自适应车道检测基准
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
AI总结：
本文提出CARLANE基准，包含三个仿真到真实域的车道检测数据集，评估多种无监督域自适应方法并揭示其与全监督方法的性能差距，以推动自动驾驶车道检测域自适应研究。
摘要：
无监督域自适应通过将模型从有标签的源域迁移到无标签的目标域，展现出缓解域偏移的巨大潜力。尽管无监督域自适应已被应用于多种复杂视觉任务，但只有少数工作关注自动驾驶中的车道检测。这可归因于缺乏公开可用的数据集。为促进这些方向的研究，我们提出了CARLANE，一个用于2D车道检测的三路仿真到真实域自适应基准。CARLANE包含单目标数据集MoLane和TuLane以及多目标数据集MuLane。这些数据集构建自三个不同的域，覆盖多样场景，共包含163K张独特图像，其中118K张带有标注。此外，我们评估并报告了系统性基线，包括我们自己的方法，该方法建立在原型跨域自监督学习之上。我们发现，与全监督基线相比，所评估的域自适应方法的假阳性和假阴性率较高。这证实了需要像CARLANE这样的基准来进一步加强车道检测的无监督域自适应研究。CARLANE、所有评估模型及相应实现均可在 https://carlanebenchmark.github.io 公开获取。
英文摘要：
Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
