GLARE: A Dataset for Traffic Sign Detection in Sun Glare
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
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DOI：
10.1109/tits.2023.3294411
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发表时间：
2022-09
影响因子：
8.5
通讯作者：
Nicholas Gray;M. Moraes;Jiang Bian;Allen Tian;A. Wang;Haoyi Xiong;Zhishan Guo
中科院分区：
文献类型：
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作者：
Nicholas Gray;M. Moraes;Jiang Bian;Allen Tian;A. Wang;Haoyi Xiong;Zhishan Guo
实时机器学习对象检测算法通常在自动驾驶汽车技术中找到，并且依赖于高质量的数据集。这些算法必须在日常条件下以及强烈的太阳眩光下正确工作。报告显示，眩光是两个最突出的与环境有关的撞车原因之一。然而，现有的数据集，如智能与安全自动驾驶交通标志实验室（丽莎）数据集和德国交通标志识别基准，根本没有反映太阳眩光的存在。本文介绍了GLARE（GLARE可在：https：//github.com/NicholasCG/GLARE_Dataset上获得）交通标志数据集：在阳光强烈视觉干扰下的美国交通标志图像集合。GLARE包含2，157张带有太阳眩光的交通标志图像，这些图像来自美国道路的33个行车记录仪视频。它为广泛使用的丽莎交通标志数据集提供了必要的丰富。我们的实验研究表明，尽管过去在没有太阳眩光的条件下，几种最先进的基线架构在交通标志检测上表现出良好的性能，但在针对GLARE进行测试时，它们表现不佳（例如，平均mAP 0.5：0.95/19.4）。我们还注意到，当前的架构在对交通标志图像进行训练时具有更好的太阳眩光性能（例如，平均mAP0.5：0.95为39.6），并且在混合条件下训练时表现最好（例如，平均mAP 0.5：0.95（42.3）。
Real-time machine learning object detection algorithms are often found within autonomous vehicle technology and depend on quality datasets. It is essential that these algorithms work correctly in everyday conditions as well as under strong sun glare. Reports indicate glare is one of the two most prominent environment-related reasons for crashes. However, existing datasets, such as the Laboratory for Intelligent & Safe Automobiles Traffic Sign (LISA) Dataset and the German Traffic Sign Recognition Benchmark, do not reflect the existence of sun glare at all. This paper presents the GLARE (GLARE is available at: https://github.com/NicholasCG/GLARE_Dataset) traffic sign dataset: a collection of images with U.S-based traffic signs under heavy visual interference by sunlight. GLARE contains 2,157 images of traffic signs with sun glare, pulled from 33 videos of dashcam footage of roads in the United States. It provides an essential enrichment to the widely used LISA Traffic Sign dataset. Our experimental study shows that although several state-of-the-art baseline architectures have demonstrated good performance on traffic sign detection in conditions without sun glare in the past, they performed poorly when tested against GLARE (e.g., average mAP0.5:0.95 of 19.4). We also notice that current architectures have better detection when trained on images of traffic signs in sun glare performance (e.g., average mAP0.5:0.95 of 39.6), and perform best when trained on a mixture of conditions (e.g., average mAP0.5:0.95 of 42.3).