Research on Traffic Sign Recognition based on Convolutional Neural Network

Traffic sign recognition has a wide application prospect in the field of automatic driving. External factors such as illumination, Angle and occlusion will affect the recognition effect of small traffic signs. In order to solve these problems, this paper designs a multi-scale fusion convolutional neural network model (SQ-RCNN) based on feature extraction network Faster RCNN. Firstly, the multi-scale Atrous Spatial Pyramid Pooling (SASPP) module is added to the basic feature extraction network. After multi-scale cavity convolution sampling, the amount of information under each feature is not changed. In this way, the loss of resolution can be reduced and the context information of the same image can be captured. Secondly, the combination structure of two convolution layers and one pooling layer in the original VGG16 model was improved, and the concat operation was adopted to enrich the number of features by merging the number of channels, so as to realize the fusion of features at different scales and improve the accuracy of identifying small targets. In addition, a dropout layer is added to prevent overfitting. The experimental results show that: In this paper, a new network structure SQ-RCNN was used to extract features from CCTSDB data set, the mean average accuracy of traffic sign identification reached 86.96%, at the same time, effectively shorten the training time.