Smart Parking Management (SPM) aims to address urban parking challenges using a machine learning model that analyzes parking area images to identify vacant spots. Employing the advanced YOLO v8 algorithm, our system achieves high precision in real-time object detection, making it an essential tool for urban parking management.
- High Precision Detection: Utilizes YOLO v8 for real-time detection with a mean Average Precision at 50% (mAP50) of 0.891.
- Robust Dataset: Analyzes 393 images from Google Maps, focusing on a variety of parking scenarios.
- User-Friendly Interface: Features a simple interface for real-time parking availability using Streamlit.
Here is a video demonstration of the parking detection in action:
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And here's an example of detected parking spaces:
Our model effectively identifies and classifies parking spaces, providing real-time updates that help reduce urban parking-related stress and congestion.
- Real-world Testing: Plans to test and optimize the model under various urban conditions.
- Expand Parking Space Diversity: Aims to enhance the model's ability to recognize different types of parking spaces.
Mahdi Banisharif |
Sadegh Jafari |