Convolutional neural network compression for traffic sign recognition

Abstract

An important component of intelligent driving technology is the recognition of traffic signs based on convolutional neural networks (CNN). How to design a traffic sign recognition system with high accuracy and good real-time performance is crucial for the safe driving of vehicles. For the current traffic sign detection algorithm, there are high network complexity, large amount of computation, and high difficulty in edge deployment. This paper proposes a deep neural network compression strategy, which skillfully uses model lightweight, pruning, knowledge distillation and quantification methods. The lightweight full connection layer is used to accelerate reasoning, and the knowledge distillation technology is innovatively used to assist the pruned student network to recover the lost accuracy. The teacher network is used to help pruning restore the original accuracy better, improve the generalization ability, and avoid that the small network cannot work after excessive pruning, so as to achieve a higher pruning rate. This experiment shows that knowledge distillation can assist pruning recovery in a more accurate manner than ordinary pruning. On the traffic sign GTSRB dataset, the mainstream network models VGGNet and AlexNet are used for training and testing. The models are compared before and after compression. Based on the results, the model can be compressed to 0.08% and will have a 97.32% accuracy.