In this paper, we present a traffic sign system using Faster R-CNN(Faster Region-Convolutional Neural Networks) for the active safety performance of automobiles. The detection algorithm (Faster R-CNN) has been improved and can be applied to traffic signs detection. Therefore, the basic network of the detection algorithm is designed using multi-scale convolution kernel ResNeXt model. Moreover, the algorithm has a multi-dimensional feature fusion strategy and it is adopted because it meets the needs of small target detection in traffic signs. In designing the RPN (Regional Proposal Network) for Faster R-CNN, anchor frames are designed by fitting traffic sign features to efficiently obtain recommended areas, and reduce false and missed detection rate. According to the experimental results in the TT100K dataset, the improved algorithm has an excellent detection effect on traffic signs under the conditions of small targets, multiple targets, and complex backgrounds, with an average accuracy of 90.83%.