Traffic Sign Recognition With Deep Learning CNN

Abstract

1. Introduction

This is my implementation of Traffic Sign Recognition Project from [](http://www.udacity.com/drive) deep neural networks and convolutional neural networks to classify traffic signs. You will train a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset. After the model is trained, you will then test your model program on new images of traffic signs you find on the web, or, if you're feeling adventurous pictures of traffic signs you find locally!

I hope this project could help people who is learning deep learning (just like me), it contains lots of pices that a beginner will ask.

I prefer to run code and observe the result rather than just reading document. This project trying to have most of the code have some how automated test covered. this is my first python project so that any pull request are welcome.

2. Objective

Traffic Sign Recognition with Deep Learning CNN

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• scikit-learn
• TensorFlow
• Matplotlib
• Pandas (Optional)
• conda install -c https://conda.anaconda.org/menpo opencv3

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jluo-bgl/Traffic-Sign-Recognition-with-Deep-Learning-CNN.git
cd Traffic-Sign-Recognition-with-Deep-Learning-CNN

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by jluo-bgl and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.