README.md

metadata

license: mit
language:
  - en
metrics:
  - mean_iou
base_model:
  - Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
  - traffic
  - parking

MAI642 Team DeepWave: Vision-Based Parking Management System Using Optimized YOLOv11

Project Overview

This project presents an innovative parking management solution using advanced computer vision and deep learning techniques. The system aims to modernize parking management by providing accurate, real-time information about parking space availability.

Problem Statement

Traditional parking systems often face challenges such as:

• Difficulty in finding available parking spaces
• Inaccurate availability information
• Long waiting times for parking

Mission

Our mission is to:

• Modernize and enhance parking management systems
• Improve customer experience
• Provide precise and accurate parking space information

Key Features

• Real-time parking space detection
• Vehicle occupancy tracking
• Optimized YOLO object detection model
• Drone-based video monitoring

Technical Approach

Model Development

• Base Model: YOLOv11
• Backbone: Custom EfficientNet integration
• Key Modifications:
  • Replaced original backbone with EfficientNet
  • Created custom configuration file (yolo11_EfficientNet.yaml)
  • Implemented core EfficientNet classes and modules

Dataset

• Source: https://universe.roboflow.com/ucy-dlyme/mai642_deep_learning-deepwave
• Data Split:
  • 70% Training
  • 20% Validation
  • 10% Testing
• Data Collection: Over 5000 images
• Data Augmentation Techniques:
  • Image flipping
  • Rotation
  • Noise addition

Performance Metrics

Model	Precision	Recall	MAP50	MAP50-95
YOLOv11s	0.958	0.933	0.971	0.757
YOLOv11s (frozen layers)	0.918	0.956	0.974	0.758
YOLOv11n (frozen layers)	0.959	0.902	0.902	0.717

Expected Benefits

• 35% Reduction in customer waiting times
• 30% Reduction in operational costs
• 23% Increase in customer satisfaction

Project Workflow

1. Data Collection and Preparation
2. Model Training and Evaluation
3. Model Configuration
4. Testing and Workflow Optimization
5. Deployment

Team Members

• Jianlin Ye: Dataset Creation, UAV Video Recording, YOLOv11 Backbone Replacement
• Rafael Koullouros: Dataset Creation, Model Training, Evaluation
• Kyriakos Pelekanos: Workflow Optimization
• Mikhail Sumskoi: HuggingFace Deployment, Basic UI

Repository

• GitHub: https://github.com/JYe9/YOLO11_EfficientNet
• HuggingFace: https://huggingface.co/jye9/DeepWave
• Dataset: https://universe.roboflow.com/ucy-dlyme/mai642_deep_learning-deepwave

Deployment

• Platform: HuggingFace (for demonstration)

Future Work

• Expand dataset
• Further optimize model performance
• Develop more comprehensive UI
• Implement wider parking management features