https://github.com/ayushgayakwad/road-sign-recognition
The Road Sign Recognition project is a real-time detection system designed to recognize road signs across 43 different classes. The project leverages the YOLOv5 model, which is trained on the GTSRB - German Traffic Sign Recognition Benchmark dataset.
https://github.com/ayushgayakwad/road-sign-recognition
computer-vision image-processing machine-learning open-source opencv python road-sign-detection road-sign-recognition road-signs traffic-sign-classification traffic-sign-recognition traffic-signs yolov5
Last synced: 7 months ago
JSON representation
The Road Sign Recognition project is a real-time detection system designed to recognize road signs across 43 different classes. The project leverages the YOLOv5 model, which is trained on the GTSRB - German Traffic Sign Recognition Benchmark dataset.
- Host: GitHub
- URL: https://github.com/ayushgayakwad/road-sign-recognition
- Owner: ayushgayakwad
- License: gpl-3.0
- Created: 2024-12-11T15:22:09.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-12-11T16:37:31.000Z (11 months ago)
- Last Synced: 2025-02-14T18:49:29.620Z (9 months ago)
- Topics: computer-vision, image-processing, machine-learning, open-source, opencv, python, road-sign-detection, road-sign-recognition, road-signs, traffic-sign-classification, traffic-sign-recognition, traffic-signs, yolov5
- Language: Python
- Homepage:
- Size: 16.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Road Sign Recognition Project
## Overview
The Road Sign Recognition project is a real-time detection system designed to recognize road signs across 43 different classes. The project leverages the YOLOv5 model, which is trained on the [GTSRB - German Traffic Sign Recognition Benchmark](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) dataset.
This system can be used to improve road safety and assist autonomous driving by identifying and interpreting road signs in real-time through a camera feed.
## Project Structure
The project folder `road-sign-recognition` includes the following subdirectories:
1. **`model`**: Contains the trained YOLOv5 model weights (`best.pt` and `last.pt`).
2. **`src`**: Contains the source code for running the project.
## Features
- Real-time detection and classification of road signs.
- Recognizes 43 distinct classes of road signs.
- Easy-to-use and customizable codebase.
## Dataset
The model is trained on the [GTSRB - German Traffic Sign Recognition Benchmark](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) dataset. This dataset contains over 50,000 images of road signs across 43 different classes, providing a robust training foundation for road sign recognition tasks.
## Requirements
Ensure you have Python installed on your system. The dependencies for the project are listed in the `requirements.txt` file.
### Steps to Install Dependencies
1. Open a terminal or command prompt.
2. Navigate to the project directory:
```
cd road-sign-recognition
```
4. Install the dependencies using the following command:
```
pip install -r requirements.txt
```
## How to Run the Project
1. Ensure you have a webcam or camera feed available.
2. Run the main script located in the **`src`** folder to start the real-time detection:
```
python src/main.py
```
3. The system will start detecting and classifying road signs in real-time. Press **`q`** to quit the application.
## Model Training Results
## Acknowledgements
- [YOLOv5](https://github.com/ultralytics/yolov5) for object detection.
- [GTSRB Dataset](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) for providing the benchmark dataset for training.