文献类型：会议论文

英文题名：Research on Traffic Sign Detection Based on Convolutional Neural Network

作者：Wang, Zhongyu[1];Guo, Hui[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：12th International Symposium on Visual Information Communication and Interaction (VINCI)

会议日期：SEP 20-22, 2019

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Convolutional neural network; Traffic sign detecion; YOLO; Region proposal network

摘要：TSD (Traffic Sign Detection) is a hotspot in autonomous driving and assisted driving research. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detecting tasks. It shows better accuracy or faster execution speed than traditional method. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved CNN model based on YOLO model, darknet 53 construction. By introducing batch normalization and RPN networks and improving the network structure for traffic sign detection tasks, the YOLO neural network detection model is optimized. The accuracy of the model in the traffic sign detection task is greatly improved, and the detection speed becomes faster. The results show that the method in this paper is of great help to improve the accuracy and detection speed of traffic sign detection and reduce the hardware requirements of the detection system as well.