交通标志检测是交通标志识别系统中的一项重要任务。与其他国家的交通标志相比，中国的交通标志有其独特的特点。卷积神经网络（CNN）在计算机视觉任务中取得了突破性进展，在交通标志分类方面取得了巨大的成功。本文提出了一种基于深卷积网络的交通标志检测算法。为了实现交通标志的实时检测，本文提出了一种基于YOLOv2的端到端卷积网络。针对交通标志的特点，在网络的中间层采用多个1×1卷积层，在顶层减少卷积层以降低计算复杂度。为了有效地检测小交通标志，我们对输入图像进行密集网格划分，得到更精细的特征图。此外，我们扩充了中国交通标志数据集（CTSD），并改进了在线可用的标志信息。根据扩展后的CTSD和德国交通标志检测基准（GTSDB）评估的所有实验结果表明该方法具有更快的速度和更强的鲁棒性。获得的最快检测速度为每图像0.017秒。

数据来源：

中国交通标志检测数据集（CCTSDB）来源于 A Real-Time Chinese Traffic Sign Detection Algorithm based on Modified YOLOv2一文提出的训练数据集。

github原文：
CSUST Chinese Traffic Sign Detection Benchmark 中国交通数据集由长沙理工大学综合交通运输大数据智能处理湖南省重点实验室张建明老师团队制作完成。
到目前为止，已经上传图像15734张，全部的groundtruth也已经上传。 声明：目前的标注数据只有三大类：指示标志、禁止标志、警告标志。
具体的细分类标准数据集，由于还在制作，暂时将不会公布，请大家关注我们的后续更新！
大家如果下载做研究实验，请尽量引用我们的文章，务必引用第一篇：
Zhang J, Jin X, Sun J, et al. Spatial and semantic convolutional features for robust visual object tracking. Multimedia Tools and Applications, 2018. https://doi.org/10.1007/s11042-018-6562-8
Zhang J, Huang M, Jin X, et al. A Real-Time Chinese Traffic Sign Detection Algorithm based on Modified YOLOv2. Algorithms, 2017, 10(4):127.
Zhang J, Huang Q, Wu H, et al. Effective traffic signs recognition via kernel PCA network. International Journal of Embedded Systems, 2018, 10(2): 120-125.
如有疑问：欢迎发送邮件： jmzhang@csust.edu.cn, lxd@stu.csust.edu.cn

论文摘要：
Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.