CURE-TSD

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.

本研究围绕挑战性场景下的交通标志识别算法鲁棒性展开探究。现有数据集在样本规模与挑战性场景覆盖范围两方面均存在局限，为此我们构建了交通标志识别挑战性虚实环境数据集（Challenging Unreal and Real Environments for Traffic Sign Recognition, CURE-TSR）。该数据集包含超200万张交通标志图像，数据源涵盖真实场景与仿真环境两类。我们基于真实场景对现有算法的性能开展基准测试，并针对挑战性场景下的性能变化进行分析。实验结果表明，挑战性场景会显著降低基线模型的识别性能，尤其当这类场景造成空间信息丢失或错位时。此外我们还探究了数据增强的作用，结果证实结合使用仿真数据与真实数据可有效提升真实场景下的平均识别精度。

搜集汇总

背景与挑战

背景概述

CURE-TSD是一个专注于交通标志识别的大规模数据集，包含200万+真实和模拟的交通标志图像，用于研究算法在挑战性条件下的鲁棒性表现。数据集特别关注空间信息丢失/错位等挑战场景，并验证了模拟器数据与真实数据结合对性能的提升作用。