关键词: 交通标志检测, 实时, 区域生成网络（RPN）, 智能交通

Abstract: In the intelligent transportation system, traffic sign recognition is required to have good robustness and real- time performance. In the actual traffic environment may be due to road signs fuzzy, light intensity, scale size, complex background and so on, resulting in traffic recognition accuracy is very low. In this paper, to solve these problems, having improve uses depth learning method to design convolutional neural networks, through the multi-level processing of convolution and pool sampling, combining the RPN network structure of the target detection method, the candidate regions of the image are extracted, so that the candidate regions are extracted, finally using connection network to return to the characteristics of figure, access to the location of the object detection and recognition. The experimental results show that the method can effectively improve the detection accuracy and computational efficiency, reduces the error rate, for traffic sign detection under adverse factors such as illumination, rotation has good stability and accuracy, effectively improves the efficiency of traffic sign recognition, has good generalization ability and adaptability, and meets the requirement of real-time.

Key words: traffic sign detection, real-time, Region Proposal Network（RPN）, intelligent transportation