Classification of Traffic Signs

Abstract

This chapter started with reviewing related work in the field of traffic sign classification. Then, it explained the necessity of splitting data and some of methods for splitting data into training, validation, and test sets. A network should be constantly assessed during training in order to diagnose it if it is necessary. For this reason, we showed how to train a network using Python interface of Caffe and evaluate it constantly using training-validation curve. We also explained different scenarios that may happen during training together with their causes and remedies. Then, some of the successful architectures that are proposed in literature for classification of traffic signs were introduced. We implemented and trained these architectures and analyzed their training-validation plots. Creating ensemble is a method to increase classification accuracy. We mentioned various methods that can be used for creating ensemble of models. Then, a method based on optimal subset selection using genetic algorithms were discussed. This way, we create ensembles with minimum number of models that together they increase the classification accuracy. After that, we showed how to interpret and analyze quantitative results such as precision, recall, and accuracy on a real dataset of traffic signs. We also explained how to understand behavior of convolutional neural networks using data-driven visualization techniques and nonlinear embedding methods such as t-SNE. Finally, we finished the chapter by implementing a more accurate and computationally efficient network that is proposed in literature. The performance of this network was also analyzed using various metrics and from different perspective.