A Comprehensive Study of Different Deep Neural Network Architectures for Traffic Sign Classification

This project presents a comparative analysis of six neural network architectures: AlexNet, DenseNet, ResNet50, ResNet101, VGGNet, and a custom CNN model, for traffic sign classification. The primary objective is to identify the architecture that achieves the highest accuracy in this task. Extensive experimentation and evaluation are conducted using a dataset of traffic sign images. The results reveal valuable insights into the performance of each architecture. ResNet50 and ResNet101 exhibit exceptional accuracy due to their deep and residual network structures. VGGNet and AlexNet achieve high accuracies by leveraging different convolutional layers and pooling operations. The custom CNN model also demonstrates promising results. This comparative analysis provides valuable knowledge about the capabilities and limitations of various neural network architectures for traffic sign classification. By identifying the architecture that achieves the highest accuracy, this study informs the optimization and selection of models for intelligent transportation systems. Enhanced traffic sign classification can significantly improve the efficiency and safety of automated driving systems.