Traffic Sign Recognition using Spatial Transformer Network with Multi Structure Convolutional Neural Network

Abstract

Convolutional Neural Network is one of state-of-the-arts and demonstrated its superior performance in various computer visions systems recently. The conventional convolutional neural network has one-way structure to train image information with fixed sizes of filters in general. However, this structure only learns image information followed by one fixed sizes of filters and this is not the best to achieve high performance of the network. In order to achieve high performance of the network, this paper suggests a novel convolutional neural network which consists of spatial transformer network and multi-structure convolutional neural network. Spatial transformer network is robust against distorted images. Multi-structure convolutional neural network uses different sizes of filters for global and local information from the given images. The proposed algorithm, spatial transformer with multi-structure convolutional neural network (SPMCNN) demonstrates its classification performance on German traffic sign recognition benchmark.