https://github.com/wasifsohail5/traffic-sign-recognition-system
The Traffic Sign Recognition System is a deep learning project focused on classifying German traffic signs from images. It leverages a custom-trained CNN model, built and deployed with Streamlit, and offers an intuitive UI for users to interact, test, and learn about traffic signs.
https://github.com/wasifsohail5/traffic-sign-recognition-system
cnn-keras deep-learning keras mobilenetv2 streamlit tensorflow traffic-sign-classification
Last synced: 3 months ago
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The Traffic Sign Recognition System is a deep learning project focused on classifying German traffic signs from images. It leverages a custom-trained CNN model, built and deployed with Streamlit, and offers an intuitive UI for users to interact, test, and learn about traffic signs.
- Host: GitHub
- URL: https://github.com/wasifsohail5/traffic-sign-recognition-system
- Owner: WasifSohail5
- Created: 2025-07-15T15:27:35.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-15T18:02:23.000Z (3 months ago)
- Last Synced: 2025-07-16T15:06:38.923Z (3 months ago)
- Topics: cnn-keras, deep-learning, keras, mobilenetv2, streamlit, tensorflow, traffic-sign-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 25.7 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚦 Traffic Sign Recognition System
A professional-grade Streamlit application for real-time, batch, and educational recognition of traffic signs using Deep Learning.
Built with modern UI, features interactive visualizations, and supports both single and batch image analysis, as well as webcam-based detection.
---
## 📜 Project Overview
The **Traffic Sign Recognition System** is a deep learning project focused on classifying German traffic signs from images. It leverages a custom-trained CNN model, built and deployed with [Streamlit](https://streamlit.io/), and offers an intuitive UI for users to interact, test, and learn about traffic signs.
---
## 📝 Description
- **Dataset Used:** [GTSRB (Kaggle)](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)
- **Goal:** Classify traffic signs based on their image using a convolutional neural network (CNN)
- **Preprocessing:** Image resizing, normalization, and data augmentation
- **Model:** Custom CNN trained to recognize multiple traffic sign classes
- **Evaluation:** Accuracy, confusion matrix, and top-k prediction confidences
---
## 🛠️ Tools & Libraries
| Python | Keras | TensorFlow | OpenCV | Streamlit | Matplotlib | Pandas |
|--------|-------|------------|--------|-----------|------------|--------|
|  |  |  |  |  |  |  |
---
## 📚 Covered Topics
- **Computer Vision (CNN)**
- **Multi-class Image Classification**
- **Real-time Detection with Webcam**
- **Batch Processing**
- **Interactive Data Visualization**
- **Streamlit UI/UX Design**
---
## ✨ Features
- **Single Image Prediction**: Upload an image and get immediate traffic sign classification with top-5 confidence scores.
- **Batch Processing**: Upload multiple images and get a detailed report with downloadable results.
- **Real-time Detection**: Use your webcam for live sign detection and recognition.
- **Traffic Sign Guide**: Learn about every supported traffic sign with visual cues and explanations.
- **Attractive, Responsive UI**: Professional dark/light theme, custom logo, clear visual feedback.
- **Performance Visualization**: See confidence levels and prediction summaries with interactive charts.
- **Educational Mode**: Explore sign categories, meanings, and test your knowledge.
---
## 🚀 Bonus
- **Data Augmentation**: Improves model generalization and accuracy.
- **Compare Models**: Evaluate custom CNN vs. pre-trained architectures (e.g., MobileNet).
- **Export Results**: Download batch prediction results as CSV for further analysis.
---
## 🖥️ App Preview
---
## 📦 Getting Started
### 1. Clone the Repository
```bash
git clone https://github.com/yourusername/traffic-sign-recognition.git
cd traffic-sign-recognition
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Download the Model
Place your trained model (`traffic_sign_cnn.h5`) in the project directory or update the path in the code.
### 4. Run the App
```bash
streamlit run traffic_sign_recog.py
```
---
## 🌐 Dataset
- **GTSRB - German Traffic Sign Recognition Benchmark**
[Kaggle Dataset Link](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)
---
## 📈 Results & Evaluation
- **Accuracy & Confusion Matrix**: Evaluated on GTSRB test set.
- **Top-5 Predictions**: Visualized in charts for each prediction.
- **Batch results**: Downloadable as CSV for further analysis.
---
## 👨💻 Author
**Wasif Sohail**
[GitHub Profile](https://github.com/Wasif-Sohail55)
---
## 🏷️ License
This project is for educational and research purposes.
---
## 🏆 Bonus Ideas
- Add more real-world images for testing robustness.
- Integrate with mobile app or Raspberry Pi for on-the-road testing.
- Extend to other countries' traffic signs.
---
## 📷 Sample Output
> *"Upload an image, and instantly know the traffic sign!"*
---
## 🙌 Acknowledgments
- GTSRB Dataset by Benchmark Authors
- [Kaggle](https://www.kaggle.com/)
- Open Source Libraries
---
