🚦 Smart Traffic Violation Detection using YOLOv8

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📌 Overview

This project is an AI-powered smart traffic monitoring system that detects traffic violations such as helmet absence and signal violations in real-time using YOLOv8 and computer vision techniques.

It is designed to assist smart city infrastructure by automating traffic surveillance and improving road safety.

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🚀 Features

• 🚗 Real-time traffic violation detection
• 🪖 Helmet detection using YOLOv8
• 🔢 Number plate detection & recognition
• 🚦 Signal violation detection
• 📊 Automated violation logging system
• ⚡ High-speed processing (20–30 FPS)

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🛠 Tech Stack

• Programming Language: Python
• Frameworks & Libraries: YOLOv8, OpenCV
• Tools: NumPy, Pandas
• Concepts: Computer Vision, Deep Learning

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📊 Performance Metrics

• ✅ Detection Accuracy: 90%+
• ⚡ Processing Speed: 20–30 FPS
• 📉 Reduced false positives by ~25%
• 📁 Trained on 1000+ annotated images

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📁 Project Structure

SMART-TRAFFIC-VIOLATION/ ├── backend/ │ ├── models/ │ ├── app.py │ ├── database.py │ ├── detection.py │ ├── number_plate.py │ ├── violation.py ├── frontend/ │ ├── assets/ │ ├── components/ │ ├── app.py ├── requirements.txt ├── README.md ├── .gitignore

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/maruthisaiteja/smart-traffic-violation-detection-using-YOLOv8
cd smart-traffic-violation-detection
pip install -r requirements.txt
Download the model from:
👉 https://github.com/ultralytics/assets/releases

Place the model file inside:

backend/
python backend/app.py

OUTPUT

🧠 How It Works

Video input is processed frame-by-frame YOLOv8 detects objects like vehicles and helmets System identifies violations (no helmet / signal jump) Number plate is extracted and recorded Violation details are stored in logs/database 🚀 Future Improvements ☁️ Cloud deployment (AWS / GCP) 📡 Live traffic camera integration 📱 Mobile app integration 🧾 Automated fine generation system 🤝 Contributing

Contributions are welcome! Feel free to fork this repository and submit a pull request.

🌍 Real-World Impact

• Helps traffic police automate violation detection
• Reduces manual monitoring effort
• Improves road safety using AI

📄 License

This project is open-source and available under the MIT License.

👨‍💻 Author

Maruthi Sai Teja 📍 Hyderabad, India