Traffic Sign Recognition

Abstract

1. Introduction

Use yolov5 for traffic sign detection Recently I am interested in doing some work related to autonomous driving, so I will try various methods to try to do work related to autonomous driving. This time I did traffic sign detection. The data set used was CCTSDB, which was obtained by Changsha University of Science and Technology expanded on the basis of the CTSDB data set. At present, Baidu Cloud Disk has uploaded 15,000 pictures, and the size of the pictures does not agree. The proportion of traffic signs has changed a lot. In fact, it is a hodgepodge of different data sets. With some data sets collected by them, traffic signs are currently only divided into three major categories, and there is no small classification.

This article is based on yolov5 for identification detection, the original version is: yoloV5

2. Objective

Use yolov5 for traffic sign detection

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/CompliceU/Traffic_Sign_Recognition.git
cd 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 CompliceU 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.