Smart Car Park Surveillance: Machine Learning - Based Object Detection in Shopping Parking Space 🚗🏢

Welcome to Smart Car Park Surveillance: Machine Learning - Based Object Detection in Shopping Parking Spaces project repository! This is my degree project showcasing a cutting-edge solution for urban parking management. Our platform is designed to assist city planners, drivers, and sustainability advocates by optimizing parking spaces through intelligent surveillance. With features like real-time car detection, parking availability insights, and congestion reduction tools, our system is your one-stop solution for enhancing urban mobility and promoting sustainable urban development.

🚀 Project Overview

This project leverages machine learning, image processing, and urban planning to optimize urban parking lot management using intelligent surveillance systems. The system employs the YOLOv8 algorithm for real-time object detection, enabling efficient identification of available parking spaces.

🔑 Key Features:

• Detection and Analysis: Utilizing the YOLOv8 algorithm, the system performs real-time image object recognition to accurately identify available parking spaces. This speeds up the parking process, reduces congestion, and enhances the user experience.
• Extensive Data Collection and Preparation: The project collected over 10,000 images, which were cleaned and pre-processed to ensure high-quality data for training and analysis.
• Sustainable Urban Development: By optimizing parking infrastructure, this project contributes to Sustainable Development Goal 9 by promoting innovative and sustainable urban technologies.

🧰 Technologies Used:

• Programming Language: Python
• Object Detection Algorithm: YOLOv8
• Libraries: OpenCV, NumPy, Ultralytics YOLOv8, etc

👍🏼 Benefits:

• Eases urban congestion by reducing the time required to find parking.
• Minimizes environmental impacts through efficient space utilization.
• Encourages smarter, more sustainable urban mobility.
• Provides a scalable and resilient solution for future urban challenges.

📄 Project Poster:

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？How to Run the Project in VSCode:

Follow these steps to set up and run this Python project on your vccode:

1. Install Python Make sure Python is installed on your system.

• If Python is not installed, download and install it from Python.

2. Install the Python Extension for VS Code

• Open VS Code.
• Go to the Extensions view by clicking on the Extensions icon in the Activity Bar or pressing Ctrl+Shift+X / Cmd+Shift+X.
• Search for “Python” and install the extension by Microsoft.

3. Clone the Repository

• Open VSCode and go to Terminal > New Terminal.
• Run the following command to clone your repository:

git clone https://github.com/your-username/urban-parking-management.git
cd urban-parking-management

• Replace your-username with your actual GitHub username.

4. Set Up the Python Environment

• Create a Virtual Environment
• It’s best to use a virtual environment for Python projects to manage dependencies separately.
• Open VSCode’s integrated terminal (if not already open) and run the following command to create a virtual environment:

python -m venv venv

• Activate the virtual environment:

source venv/bin/activate

• Once the virtual environment is activated, you should see (venv) at the beginning of the terminal prompt.
• Run the following command to install the required dependencies:

pip install -r requirements.txt

5. Download YOLOv8 weights and download the YOLOv8 pre-trained weights:

• Install the ultralytics package:

pip install ultralytics

6. Verify Dataset

• Ensure that your dataset is in the proper folder (e.g., data/images/ for the image files and data/labels/ for the label files). The dataset should be configured correctly in your project (likely in a YAML file or similar).

7. Run the app.py Script

• Run it directly from the terminal by typing:

python app.py

8. Access the Application

• If app.py is running a web-based application (e.g., using Flask or FastAPI), you should be able to open a browser and go to the address http://localhost:5000/ (or whatever port your app is configured to run on). This should bring up the interface where you can interact with the parking management system.
• If your project uses something like a GUI or a different interface, follow the on-screen instructions to interact with the system.