Object Detection Parking Spot

Abstract

1. Introduction

The application uses YOLO V8, a state-of-the-art object detection model, to classify parking spaces as either occupied or empty, offering real-world benefits in smart parking systems, traffic management, and resource optimization.

This applications focuses on object detection for parking spot availability using the YOLO (You Only Look Once) V8 architecture. The dataset consists of annotated parking lot images under various weather conditions, with classes identifying empty and occupied parking spaces.

The provided dataset includes labeled images of parking lots captured under different weather conditions. The labels identify parking spaces as either occupied or empty using bounding boxes.

2. Objective

This application focuses on detecting parking spot availability using the YOLO V8 architecture, leveraging a labeled dataset of parking lot images under various weather conditions for smart parking, traffic management, and resource optimization applications.

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

3. Key Features / Modules

• Data preparation: Preprocess the dataset.
• Training: Train a YOLO V8 model.
• Evaluation: Evaluate the YOLO V8 model.
• Inference: Run inference on random test images.

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/your-ai-solution/object-detection-parking-spot.git
cd object-detection-parking-spot

1. Clone the repository:
2. Create a Conda environment:
3. Install dependencies:
4. Build the Docker image:
5. Run the Docker container:

git clone https://github.com/your-ai-solution/object-detection-parking-spot.git
   cd object-detection-parking-spot

conda env create -f environment.yml
   conda activate object-detection-parking-spot

pip install -r requirements.txt

docker build -t object-detection-parking-spot .

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 your-ai-solution and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.