Traffic Sign Recognition by Deep Learning

Abstract

Convolutional Neural Network is a common way to address image recognition problems in recent years. Among these years, some new ideas which related to CNN is found, such as dropout and batch normalization. In this project, CNN models are built based on two famous model architecture. Initialization of parameters are chosen carefully. Experiments were done on different architectures and parameters. Results are found which match the mathematical and statistical theories supported behind.