Research on Detection and Recognition of Traffic Signs Based on Convolutional Neural Networks

Abstract

The road traffic sign detection and identification (TSDR) system is a subsystem of the advanced driver assistance system. It has received extensive attention from domestic and foreign researchers. The TSDR system is mainly composed of two parts: the detection of traffic signs and the identification and output of traffic signs. This paper focuses on the research of TSDR system and proposes a method based on faster R-CNN combined with part of the low-level feature map. The lightweight model MobileNet is used to finish the traffic sign detection. Experiments show that this method has a certain effect on the detection of small-scale traffic signs, and the detection speed is fast. The LeNet5 and Inception V3 network model are used for traffic sign recognition. The model is optimized by adjusting parameters such as the learning rate and batch size. It shows that for LeNet5, when the learning rate = 0.001, the recognition rate can reach 92.5%. For the Inception V3 model, when the learning rate is 0.005, it has a higher recognition rate than the LeNet5 model by 4.9 percentage points.