🚗 Smart Parking Space Detector using YOLO and OpenCV 🅿️

🔍 Overview

This project utilizes YOLO (You Only Look Once) and OpenCV to detect and monitor parking spaces in real-time. It helps optimize parking lot management by identifying vacant and occupied spots with high accuracy.

🛠️ Features

✅ Real-time Parking Detection using YOLOv4/YOLOv8
✅ OpenCV for Image Processing
✅ Bounding Boxes & Object Detection for parked cars
✅ Customizable Parking Zone Mapping
✅ Supports Live Camera Feeds & Video Input

📸 Demo

🏗️ Installation

1️⃣ Clone the Repository

git clone https://github.com/prajesdas/Smart-Parking-Space-Detector-using-YOLO-and-OpenCV.git
cd Smart-Parking-Space-Detector-using-YOLO-and-OpenCV

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Download YOLO Weights

• Download YOLOv4 weights from official YOLO website or use a pre-trained YOLOv8 model.
• Place the weights in the models/ directory.

4️⃣ Run the Detection Script

python detect_parking.py --source video.mp4

or for live webcam feed:

python detect_parking.py --source 0

📂 Project Structure

📂 Smart-Parking-Space-Detector  
 ┣ 📂 models/              # YOLO model weights  
 ┣ 📂 data/                # Parking lot images/videos  
 ┣ 📂 utils/               # Helper functions  
 ┣ 📜 detect_parking.py    # Main script for detection  
 ┣ 📜 requirements.txt     # Dependencies  
 ┣ 📜 README.md            # Project documentation  

🚀 Future Enhancements

🔹 Integration with a mobile app for user-friendly access 📱
🔹 Adding a cloud database to store parking statistics ☁️
🔹 Implementing number plate recognition for security 🔢

🤝 Contributing

Pull requests are welcome! If you'd like to contribute, please open an issue first to discuss your ideas.

📜 License

This project is MIT Licensed. Feel free to use and modify it.

📧 Contact

For any queries, reach out to Prajes Das via GitHub.

🔗 GitHub Repository