Traffic Sign Classification with Convolutional Neural Networks

Overview

The "Traffic Sign Classification" project introduces an innovative approach to improve road safety and traffic management through the application of Convolutional Neural Networks (CNNs). As vehicular traffic continues to grow exponentially, the accurate and real-time recognition of traffic signs becomes crucial for preventing accidents and ensuring orderly traffic flow. Leveraging the power of CNNs, we have developed a robust and efficient traffic sign recognition system capable of accurately identifying and classifying a diverse range of traffic signs commonly found on roads and highways.

Dataset and Preprocessing

Throughout the project, we collected and preprocessed a comprehensive dataset of traffic sign images, incorporating diverse variations in lighting conditions, weather scenarios, and occlusions. This dataset preparation ensures that our model can handle real-world challenges and deliver reliable results.

Model Architecture

The heart of our project is the Convolutional Neural Network model, specifically designed for traffic sign classification. The model architecture is as follows:

• Convolutional Layer 1: 60 filters of size (5,5) with ReLU activation
• Convolutional Layer 2: 60 filters of size (5,5) with ReLU activation
• Max Pooling Layer: Pooling size (2,2)
• Convolutional Layer 3: 15 filters of size (3,3) with ReLU activation
• Max Pooling Layer: Pooling size (2,2)
• Dropout Layer: Dropout rate of 0.5 to reduce overfitting
• Flatten Layer
• Dense Layer 1: 500 units with ReLU activation
• Dropout Layer: Dropout rate of 0.5
• Dense Layer 2: Output layer with softmax activation (number of classes = 43 in this case, but it can be adapted for different datasets)

The model is compiled using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss, and we track the accuracy as a performance metric.

Model Performance

Our CNN model exhibited remarkable performance during training:

• Achieved an accuracy of 95.79% in just 8 epochs.
• Achieved a low loss value of 0.1337.

This high accuracy and low loss demonstrate the model's effectiveness in accurately identifying and classifying traffic signs.

Conclusion

Overall, this project serves as a robust foundation for ongoing research and development in the field of computer vision and deep learning. It holds the potential to revolutionize the future of transportation and road safety by ensuring accurate traffic sign recognition, ultimately contributing to safer and more efficient roadways.