Vehicle-Detection-And-Counting

A vehicle detection and counting system using YOLOv11 and Ultralytics' ObjectCounter solution. This project includes a Streamlit web app for uploading and analyzing video/image input, along with standalone Python scripts for video and image processing.

🚀 Features

• Real-time vehicle detection and counting using YOLOv11
• Streamlit web interface for user-friendly interaction
• Support for both video and image inputs
• Standalone scripts for batch processing of media files
• Visual representation of detection and counting results

📁 Project Structure

Vehicle-Detection-And-Counting/
├── assets/                 # Directory for storing assets like images or icons
├── app.py                  # Streamlit web application script
├── image_counting.py       # Script for processing and counting vehicles in images
├── video_counting.py       # Script for processing and counting vehicles in videos
├── requirements.txt        # Python dependencies
├── short_clip.mp4          # Sample video for testing
├── yolo11n.pt              # Pre-trained YOLOv11 model weights
└── README.md               # Project documentation

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/Badaszz/Vehicle-Detection-And-Counting.git
   cd Vehicle-Detection-And-Counting

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

🖥️ Usage

Streamlit Web Application

Launch the web application using Streamlit:

streamlit run app.py

This will open a web interface where you can upload images or videos for vehicle detection and counting.

Command-Line Scripts

• Process an image:

  python image_counting.py --image_path path_to_image.jpg

• Process a video:

  python video_counting.py --video_path path_to_video.mp4

Replace path_to_image.jpg and path_to_video.mp4 with the actual paths to your media files.

🧠 Model Details

The project employs YOLOv11, a state-of-the-art object detection model, in conjunction with Ultralytics' ObjectCounter for accurate vehicle detection and counting. The pre-trained model weights (yolo11n.pt) are included in the repository.

📸 Sample Output

Sample outputs can be found in the assets folder

🤝 Contributing

Contributions are welcome! If you have suggestions for improvements or encounter any issues, please open an issue or submit a pull request.

📬 Contact

For any inquiries or feedback, please contact [[email protected]].