In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) dataset. It includes more than two million traffic sign
images that are based on real-world and simulator data. We benchmark the
performance of existing solutions in real-world scenarios and analyze the
performance variation with respect to challenging conditions. We show that
challenging conditions can decrease the performance of baseline methods
significantly, especially if these challenging conditions result in loss or
misplacement of spatial information. We also investigate the effect of data
augmentation and show that utilization of simulator data along with real-world
data enhance the average recognition performance in real-world scenarios. The
dataset is publicly available at this https URL

研究交通标志识别算法在挑战性条件下的鲁棒性，并构建了基于真实世界和模拟数据的 CURE-TSR 数据集，分析了现有解决方案在真实世界场景下的性能，并显示出挑战性条件会显著降低基线方法的性能。此外，我们还调查了数据增强的效果，并表明与真实世界数据一起利用模拟数据可以增强真实世界场景中的平均识别性能。