In this paper, we are reporting a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. We used HOG and SVM in the detection part to detect the road signs captured by the camera. Then we used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. Our system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part.

在本文中，我们报告了一种用于道路标志检测和分类的系统。该系统由两部分组成。第一部分实时检测路标。第二部分对德国交通标志（GTSRB）数据集进行分类，并使用第一部分中检测到的道路标志进行预测以测试有效性。我们在检测部分使用了HOG和SVM来检测摄像机捕获的路标。然后，我们使用了基于LeNet模型的卷积神经网络，其中在分类部分中进行了一些修改。我们的系统在检测部分的准确率为96.85％，在分类部分的准确率为96.23％。