CompletedPythonYOLOv8CNN+2 more
Drone Detection
YOLOv8-based drone detection system with real-time alerting and edge deployment capabilities.
Timeline
1 month
Role
ML Engineer
Team
Solo
Status
CompletedTechnology Stack
Python
YOLOv8
CNN
OpenCV
PyTorch
Key Challenges
- Real-time inference
- Edge deployment
- False alarm reduction
- Dataset preprocessing
Key Learnings
- YOLOv8 training
- Object detection pipelines
- Edge optimization
- Live alerting systems
Drone Detection System
Overview
A YOLOv8-based drone detection system trained on 2,000 images, achieving high accuracy with real-time edge deployment capabilities.
Performance Metrics
- mAP@0.5: 0.90
- Precision: 0.92
- Recall: 0.89
- FPS: 15 on edge device
Key Features
- Real-time Detection: Live drone detection from camera feeds
- Edge Deployment: Optimized for edge devices
- Live Alerting: Built-in preprocessing and live alerting to reduce false alarms
- High Accuracy: Trained on curated dataset for reliable detection
Tech Stack
- Python for development
- YOLOv8 for object detection
- CNN for feature extraction
- OpenCV for image processing
- PyTorch for deep learning
Training Details
- Trained on 2,000 labeled drone images
- Custom preprocessing pipeline
- Data augmentation for robustness
- Optimized for real-world deployment