Lightweight Spatial Pyramid Convolutional Neural Network for Traffic Sign Classification
Reza Fuad Rachmadi, Gou Koutaki, Kohichi Ogata · 2018
In this paper, we proposed a lightweight spatial pyramid convolutional neural network (SP-CNN) classifier for image-based traffic sign classification. The lightweight SP-CNN classifier is formed based on ResNet (residual network) CNN architecture which originally used for CIFAR10 image classification problems. Our proposed classifier consists of five parallel convolutional networks and each network processes a cropped region using spatial pyramid configuration. For smoother transitions between the regions cropped in the level 1 of spatial pyramid configuration, we overlap the level 1 of spatial pyramid regions configuration for around 12.5% on each axis. The proposed classifier trained by fine-tuning the CIFAR10 weights with NAG (Nesterov Accelerated Gradient) training algorithm. Experiments on GTSRB (German Traffic Sign Recognition Benchmark) dataset show that our lightweight SP-CNN version produces an accuracy of 99.70% and an execution time of 60 ms. The proposed classifier produces a very competitive accuracy compared with other methods but with less number of parameters.