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.
翻译:无人监督的域域适应显示,通过将模型从标签源域向无标签的目标域转移,有巨大的潜力减轻域变。虽然无人监督的域适应应用到各种各样的复杂视觉任务,但很少工作侧重于自动驾驶的车道探测,这可归因于缺乏公开可用的数据集。为了便利这些方向的研究,我们提议CARLANE,一个用于2D车道探测的3-way sim-to-al-al-ale适应基准CARLANE。CARLANE包括单一目标数据集MoLane和TuLane以及多目标数据集MuLane。这些数据集来自三个不同领域,涵盖不同的场景,总共包含163K的独特图像,其中118K是附加说明的。此外,我们评价和报告系统基线,包括我们自己的方法,它建立在Protogramid cros-domain自我监督的学习上。我们发现,与完全监督的基线相比,被评估的域适应方法的正反率和假负率很高。这证实了CARLANE模型等基准需要进一步加强在未受监督的CARLAN/DOmamamainal的公开检测。