mlsanityData to 14 Oct 2026

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

arXiv 1712.02463 ↗First posted 7 Dec 2017cs.CVcited by 32

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Abstract (arXiv)

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 https://ghassanalregib.com/cure-tsr/.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2017.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 6; 2024: 6; 2025: 6; 2026: 3.

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