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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- mean_iou |
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base_model: |
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- Ultralytics/YOLO11 |
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pipeline_tag: object-detection |
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tags: |
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- traffic |
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- parking |
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--- |
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# MAI642 Team DeepWave: Vision-Based Parking Management System Using Optimized YOLOv11 |
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## Project Overview |
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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. |
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## Problem Statement |
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Traditional parking systems often face challenges such as: |
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- Difficulty in finding available parking spaces |
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- Inaccurate availability information |
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- Long waiting times for parking |
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## Mission |
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Our mission is to: |
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- Modernize and enhance parking management systems |
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- Improve customer experience |
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- Provide precise and accurate parking space information |
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## Key Features |
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- Real-time parking space detection |
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- Vehicle occupancy tracking |
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- Optimized YOLO object detection model |
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- Drone-based video monitoring |
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## Technical Approach |
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### Model Development |
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- Base Model: YOLOv11 |
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- Backbone: Custom EfficientNet integration |
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- Key Modifications: |
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- Replaced original backbone with EfficientNet |
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- Created custom configuration file (yolo11_EfficientNet.yaml) |
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- Implemented core EfficientNet classes and modules |
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### Dataset |
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- Source: https://universe.roboflow.com/ucy-dlyme/mai642_deep_learning-deepwave |
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- Data Split: |
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- 70% Training |
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- 20% Validation |
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- 10% Testing |
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- Data Collection: Over 5000 images |
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- Data Augmentation Techniques: |
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- Image flipping |
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- Rotation |
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- Noise addition |
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## Performance Metrics |
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| Model | Precision | Recall | MAP50 | MAP50-95 | |
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|-------|-----------|--------|-------|----------| |
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| YOLOv11s | 0.958 | 0.933 | 0.971 | 0.757 | |
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| YOLOv11s (frozen layers) | 0.918 | 0.956 | 0.974 | 0.758 | |
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| YOLOv11n (frozen layers) | 0.959 | 0.902 | 0.902 | 0.717 | |
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## Expected Benefits |
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- 35% Reduction in customer waiting times |
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- 30% Reduction in operational costs |
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- 23% Increase in customer satisfaction |
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## Project Workflow |
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1. Data Collection and Preparation |
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2. Model Training and Evaluation |
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3. Model Configuration |
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4. Testing and Workflow Optimization |
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5. Deployment |
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## Team Members |
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- Jianlin Ye: Dataset Creation, UAV Video Recording, YOLOv11 Backbone Replacement |
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- Rafael Koullouros: Dataset Creation, Model Training, Evaluation |
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- Kyriakos Pelekanos: Workflow Optimization |
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- Mikhail Sumskoi: HuggingFace Deployment, Basic UI |
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## Repository |
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- GitHub: https://github.com/JYe9/YOLO11_EfficientNet |
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- HuggingFace: https://huggingface.co/jye9/DeepWave |
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- Dataset: https://universe.roboflow.com/ucy-dlyme/mai642_deep_learning-deepwave |
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## Deployment |
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- Platform: HuggingFace (for demonstration) |
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## Future Work |
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- Expand dataset |
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- Further optimize model performance |
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- Develop more comprehensive UI |
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- Implement wider parking management features |
