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Traffic Light Management System using YOLO and OpenCV

Overview

This project is a Traffic Light Management System that uses a YOLO-based object detection model and OpenCV to monitor traffic density and dynamically control traffic lights. The system analyzes real-time camera input to detect vehicles and adjust the traffic signals accordingly, improving traffic efficiency.

Features

  • Uses YOLO (You Only Look Once) for real-time vehicle detection.
  • Implements OpenCV for video processing and overlaying traffic light visuals.
  • Supports multi-threading for smooth processing.
  • Detects traffic congestion levels and changes traffic lights dynamically.
  • Displays real-time vehicle count and traffic status messages.
  • Logs system events for debugging and monitoring.

Technologies Used

  • Python
  • OpenCV (for image processing and video capture)
  • YOLO (Ultralytics) (for object detection)
  • Threading & ThreadPoolExecutor (for parallel processing)
  • Logging & JSON Configuration

System Requirements

  • Python 3.7+
  • OpenCV (opencv-python)
  • Ultralytics YOLO (ultralytics)
  • NumPy
  • A webcam (or a pre-recorded video feed for testing)

Installation

  1. Clone the repository

    git clone https://github.com/Burhanali2211/Traffic-Light-Management-System.git
cd traffic-light-system

  2. Install dependencies

    pip install -r requirements.txt

  3. Download YOLO model Download a YOLO model (e.g., yolov8n.pt) from Ultralytics YOLO and place it in the project directory.

  4. Modify config.json Configure settings like camera index, model path, and traffic thresholds.

Configuration (config.json)

{
  "model_path": "yolov8n.pt",
  "camera_index": 0,
  "vehicle_classes": [2, 3, 5, 7],
  "high_traffic_threshold": 10,
  "medium_traffic_threshold": 5,
  "min_state_duration": 5
}
  • vehicle_classes: Object classes representing vehicles (YOLO classes: car=2, motorbike=3, bus=5, truck=7)
  • high_traffic_threshold: Number of vehicles required to trigger red light
  • medium_traffic_threshold: Number of vehicles required to trigger yellow light
  • min_state_duration: Minimum duration before switching traffic lights

Usage

Run the script using:

python app.py
  • Press q to exit the program.

How It Works

  1. Captures video frames from the camera.
  2. Processes frames using YOLO to detect vehicles.
  3. Counts detected vehicles and determines traffic congestion level.
  4. Changes the traffic light state based on congestion.
  5. Overlays traffic light visuals and vehicle count on the video feed.
  6. Displays the live feed with updated traffic conditions.

Demo

Ensure the camera is connected and configured properly in config.json. When running, the system will:

  • Show the live feed with a traffic light overlay.
  • Update traffic lights dynamically based on vehicle count.
  • Log status messages for debugging.

Logging

Logs are saved in traffic_system.log for tracking events and debugging issues.

Future Improvements

  • Integrate with Raspberry Pi for real-world deployment.
  • Use multiple cameras for large-scale traffic management.
  • Enhance YOLO model training with a custom dataset for better accuracy.
  • Implement automated reports based on traffic patterns.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

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