Dronevade
On-device drone detection system using computer vision for security and wildfire safety
Project Status: Active Development - Core AI models completed, edge deployment in progress
Project Overview
The Challenge
Traditional drone detection systems rely on expensive radar equipment and require significant infrastructure. Security personnel and emergency responders need a cost-effective, portable solution that can detect drones in real-time using existing camera systems.
The system needed to work in various lighting conditions, handle different drone types and sizes, and provide immediate alerts without requiring cloud connectivity.
The Solution
Dronevade uses custom-trained computer vision models to detect drones in real-time video feeds. The AI system processes video on edge devices, providing immediate threat assessment without requiring internet connectivity or expensive hardware.
The system can be deployed on existing security cameras, mobile devices, or dedicated edge computing units, making it accessible for various security and safety applications.
AI/ML Architecture
Model Architecture
Computer Vision Pipeline
- • Real-time video frame processing
- • Multi-scale object detection
- • Temporal consistency filtering
- • Background subtraction algorithms
Neural Network
- • Custom YOLO-based architecture
- • Transfer learning from COCO dataset
- • Optimized for edge deployment
- • Quantized model for efficiency
Edge Processing
- • TensorFlow Lite deployment
- • GPU acceleration support
- • Low-latency inference pipeline
- • Memory-optimized processing
Model Performance Metrics
Technical Stack
AI/ML Technologies
Development & Deployment
Use Cases & Applications
Wildfire Safety
Monitor restricted airspace during firefighting operations. Detect unauthorized drones that could interfere with firefighting aircraft and emergency response efforts.
Security Monitoring
Protect critical infrastructure, government facilities, and corporate campuses. Real-time detection of surveillance or malicious drone activity.
Airport Security
Monitor airport perimeters and restricted airspace. Detect drones that could pose safety risks to aircraft operations and passenger safety.
Development Process
Data Collection & Annotation
Collected thousands of drone images and videos from various angles, lighting conditions, and drone types. Manually annotated dataset with bounding boxes for training.
Model Training & Optimization
Trained custom YOLO model using transfer learning. Optimized for edge deployment through quantization and architecture modifications for speed and accuracy.
Edge Deployment & Testing
Deployed model on various edge devices including Raspberry Pi, NVIDIA Jetson, and mobile devices. Conducted extensive field testing in real-world conditions.
Performance Optimization
Implemented temporal filtering, multi-threading, and GPU acceleration. Achieved sub-3ms inference times while maintaining high accuracy.
Future Enhancements
Advanced AI Features
- • Drone classification by type and model
- • Intent prediction and threat assessment
- • Multi-camera fusion for 3D tracking
- • Automated alert systems and reporting
Platform Expansion
- • Mobile app for field deployment
- • Cloud-based analytics dashboard
- • Integration with existing security systems
- • API for third-party integrations
Interested in AI-Powered Security Solutions?
Let's discuss how computer vision and edge AI can enhance your security and safety operations.