Application of Improved LeNet-5 Network in Traffic Sign Recognition

Considering that most convolutional neural network (CNN) models designed for traffic sign recognition (TSR) have sacrificed more resources and complicated network model development while pursuing higher performance, LeNet-5 shallow CNN with low complexity has been selected for improvement. Increasing the number of convolution kernel in the first convolution layer (C1 layer) and the third convolution layer (C3 layer) while reducing the size of the convolution kernel in C3 layer. Introducing Rectified Linear Unit (ReLU) function with better performance. The maximum pooling is introduced instead of mean pooling. Besides, the output layer employs support vector machine (SVM) to shorten the operation time. The research results demonstrate that the improved LeNet-5 network has an identification accuracy rate of 98.12% and the identification time is 0.154s for traffic signs in German Traffic Sign Recognition Benchmark (GTSRB), which could guarantee the real-time performance of the system and effectively reduce the complexity of the system on the basis of a high recognition rate.