Car Parking Detection

Abstract

1. Introduction

This project provides a production-ready parking space detection system that combines classical computer vision techniques with state-of-the-art deep learning (YOLOv8) to analyze parking lots in real-time.

2. Objective

AI-powered parking space detection using YOLOv8 and OpenCV. Identifies empty or occupied spots from images or video, supports interactive region selection, real-time detection, and auto-generated analytics reports. Suitable for smart parking systems, academic work, and computer vision learning. Python 3.8+.

This project demonstrates how Python, Computer Vision, OpenCV can be applied to a real-world Internet of Things (IoT) problem.

3. Key Features / Modules

• Special parking space detection (yellow markings)
• Video loop playback
• Precise coordinate tracking
• Robust error handling
• Comprehensive logging
• Centralized configuration

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/8harath/Car-Parking-Detection.git
cd Car-Parking-Detection

# 1. Clone the repository
git clone https://github.com/8harath/Car-Parking-Detection.git
cd Car-Parking-Detection

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the application
python run.py --image carParkImg.jpg

# Open your terminal/command prompt

# Navigate to where you want to save the project
cd Desktop  # or any folder you prefer

# Download the project
git clone https://github.com/8harath/Car-Parking-Detection.git

# Enter the project folder
cd Car-Parking-Detection

# Install required libraries
pip install -r requirements.txt

# Start the program
python run.py --image carParkImg.jpg

# Test imports
python -c "import cv2, numpy, pandas, ultralytics; print('✓ All dependencies installed')"

# Check GPU availability (optional)
python -c "import torch; print('CUDA available:', torch.cuda.is_available())"

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add a mobile dashboard using Blynk or Firebase
• Store readings in a cloud database for history charts
• Add alerts via SMS / Telegram when thresholds are crossed

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which microcontroller / board and sensors are used and why?
2. How does the device send data (Wi-Fi, MQTT, HTTP, Bluetooth)?
3. Where is the sensor data stored and visualised?
4. How is power consumption managed?
5. How would you secure the device and its communication?

9. Source Code & License

This project is developed by 8harath and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.