---
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