As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

作为智能交通系统（ITS）的一部分，智能交通研究人员越来越重视车辆交通标志检测与识别系统，基于卷积神经网络的交通标志检测与识别算法在可扩展性和鲁棒性方面具有很大的优势，但是它的优化空间仍然存在很大的误差，计算和存储空间。在本文中，我们设计了一种用于交通标志检测和识别的多尺度特征融合算法。为了提高网络的准确性，在损失函数中使用了高斯分布特性。在清华-腾讯开放交通标志数据集上，对两个具有不同特征量度和YOLOv3-tiny的神经网络进行了训练和分析。实验结果表明，具有多个特征量尺度的网络对目标的检测和识别有了显着提高，查全率和查全率分别为95.32％和93.13％。最后，在NVIDIA Jetson Tx2平台上验证了交通标志检测和识别算法，并提供28 fps的出色性能。