š©ļø UAV Detection System
Overview
An advanced drone detection system combining a Next.js web interface, deep learning models, and synthetic dataset generation using Blender. This project is designed to detect UAVs (Unmanned Aerial Vehicles) in real-time, offering high accuracy and adaptability across various scenarios.
šÆ System Overview
The UAV Detection System is a comprehensive solution for detecting rones. It integrates state-of-the-art computer vision models with a user-friendly web interface, providing a versatile tool for drone surveillance in diverse environments.
āØ Features
Detection Modes:
- Photo analysis
- Video processing
- Real-time camera stream detection
š ļø Building Blocks of the UAV Detection System
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Data Pipeline:
- Synthetic data generation with Blender
- Augmentation for enhanced model robustness
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Model:
- YOLO-based architectures fine-tuned for UAV detection
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Web Interface:
- Next.js for real-time visualization and user interaction
šØ Dataset Generation
Blender Pipeline
The dataset creation process employs Blender for rendering diverse and realistic UAV scenarios:
| Category | Details |
|---|---|
| Scene Setup |
š§ Model Training & Testing
The model training pipeline uses a YOLOv10-based architecture optimized for drone detection. Metrics, checkpoints, and configurations are saved for reproducibility.
Training Results
Testing Results
| Metric | Performance on Test Data | Performance on Validation Data | Difference in Performance |
|---|
Ground Truth Comparisons
| Ground Truth vs Test Images | Ground Truth vs Real Images |
š License
This project is licensed under the MIT License. See the LICENSE file for more details.