Traffic Sign Recognition Py Qt5 YOLOv5 GUI

Abstract

1. Introduction

Road Sign Recognition Project Based on YOLOv5 (YOLOv5 GUI)

This system is a road sign recognition application leveraging YOLOv5 . It employs a MySQL database , PyQt5 for the interface design , PyTorch deep learning framework. Additionally, it incorporates CSS styles .

The entire system is designed to support various data input methods and model switching. Additionally, it offers image enhancement techniques such as mosaic and mixup .

2. Objective

This YOLOv5🚀😊 GUI road sign system uses MySQL💽, PyQt5🎨, PyTorch, CSS🌈. It has modules for login🔑, YOLOv5 setup📋, sign recognition🔍, database💾, and image processing🖼️. It supports diverse inputs, model switching, and enhancements like mosaic and mixup📈.

This project demonstrates how Python, PyTorch 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/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI.git
cd Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI

pip install -r requirements.txt

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 Ai-trainee and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.