Real Time German Traffic Sign Recognition

Abstract

1. Introduction

This project is a basic real-time german traffic sign recognition application with deep learning techniques. It uses one camera inside the car to localize and classify the traffic sign in real time.

There are not many open training data for German traffic signs. Datasets used here are: GTSDB and GTSRB.

Different than standard end-to-end object detection algorithms, in this project a pipeline is built, localization and classification tasks are handled separately.

2. Objective

This project is a basic real-time german traffic sign recognition application with deeplearning techniques.

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

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/helloyide/real-time-German-traffic-sign-recognition.git
cd real-time-German-traffic-sign-recognition

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 helloyide 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.