CURE-TSD: Challenging Unreal and Real Environment for Traffic Sign Detection

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the CURE-TSD dataset are grouped into two classes: real data and unreal data. Real data correspond to processed versions of sequences acquired from real world. Unreal data corresponds to synthesized sequences generated in a virtual environment. There are 49 real sequences and 49 unreal sequences that do not include any specific challenge. We separated the sequences into 70% and 0 splits. Therefore, we have 34 training videos and 15 test videos in both real and unreal sequences that are challenge-free. There are 300 frames in each video sequence. There are 49 challenge-free real video sequences processed with 12 different types of effects and 5 different challenge levels, which result in 2,989 (49125+49) video sequences. Moreover, there are 49 synthesized video sequences processed with 11 different types of effects and 5 different challenge levels, which leads to 2,744 (49115+49) video sequences. In total, there are 5,733 video sequences, which include around 1.72 million frames. Please refer to our GitHub page for code, papers, and more information.作为佐治亚理工学院OLIVES实验室的研究方向之一，我们致力于探索数据驱动算法在各类实际部署场景下的鲁棒性，即训练得到的模型在真实部署环境中可能面临的复杂挑战性条件下的表现稳定性。为达成这一研究目标，我们构建了一个大规模（约172万帧）的交通标志检测视频数据集CURE-TSD，该数据集是目前涵盖可控合成挑战性条件的最全面数据集之一。CURE-TSD数据集中的视频序列分为两类：真实数据与虚拟数据。真实数据取自真实世界采集的序列并经过后期处理，虚拟数据则为在虚拟环境中生成的合成序列。其中包含49组无特定挑战性条件的真实序列与49组无特定挑战性条件的虚拟序列。我们将此类无挑战性条件的序列划分为70%训练集与0%拆分，因此在无挑战性条件的真实与虚拟序列中，共得到34条训练视频与15条测试视频。每条视频序列包含300帧。针对49组无挑战性条件的真实序列，我们通过12种不同类型的效果与5种不同的挑战性等级进行处理，最终得到2989（49×12×5 + 49）条视频序列。此外，针对49组合成视频序列，我们通过11种不同类型的效果与5种不同的挑战性等级进行处理，最终得到2744（49×11×5 + 49）条视频序列。总体而言，该数据集共包含5733条视频序列，总计约172万帧。如需获取代码、论文及更多相关信息，请参阅我们的GitHub页面。

搜集汇总

背景与挑战

背景概述

CURE-TSD是一个用于交通标志检测的大规模视频数据集，包含约172万帧图像，涵盖真实和合成环境下的多种挑战性条件（如模糊、噪声、雨雪等），旨在评估算法在复杂场景中的鲁棒性。数据集包含5733个视频序列，每个序列有300帧，并提供了详细的注释和训练/测试划分，适用于自动驾驶和高级驾驶辅助系统的研究。