| --- |
| 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 |
