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Inside"],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"screenshots/falcon_logo.png\" alt=\"FALCON Banner\" width=\"100%\"\u003e\n\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003e🦅 FALCON\u003c/h1\u003e\n\n\u003ch3 align=\"center\"\u003e\nAutomatic Crime Alert and Reporting System\n\u003c/h3\u003e\n\n\u003cp align=\"center\"\u003e\nReal-Time Weapon Detection using YOLOv8 • Flask • OpenCV • Raspberry Pi • Twilio\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n\n![Python](https://img.shields.io/badge/Python-3.10.11-blue)\n![Flask](https://img.shields.io/badge/Flask-Web%20Framework-black)\n![YOLOv8](https://img.shields.io/badge/YOLOv8-Ultralytics-red)\n![OpenCV](https://img.shields.io/badge/OpenCV-Computer%20Vision-green)\n![SQLite](https://img.shields.io/badge/Database-SQLite-blue)\n![Twilio](https://img.shields.io/badge/Twilio-SMS-red)\n![License](https://img.shields.io/badge/License-MIT-success)\n\n\u003c/p\u003e\n\n---\n\n# 📑 Table of Contents\n\n- Overview\n- Problem Statement\n- Solution\n- Features\n- System Architecture\n- Technology Stack\n- Project Structure\n- Screenshots\n- Installation\n- Configuration\n- Running the Project\n- Workflow\n- Model Performance\n- Security\n- Limitations\n- Future Roadmap\n- Contributing\n- License\n- Author\n\n---\n\n# 📖 Overview\n\nFALCON is an AI-powered surveillance and emergency response platform that performs **real-time weapon detection** using a custom-trained YOLOv8 model. The system automatically identifies potential threats from live video streams, stores incident data, captures evidence, updates an interactive dashboard, and immediately notifies authorities through Twilio SMS alerts.\n\nDesigned as an end-to-end solution, FALCON combines computer vision, backend engineering, edge AI, and web technologies into a complete security platform suitable for educational demonstrations, research, and smart surveillance prototypes.\n\n---\n\n# ❗ Problem Statement\n\nTraditional CCTV systems continuously record video but rely on human operators to identify threats, often delaying emergency response.\n\nFALCON addresses this challenge by automatically detecting weapons in live video streams and triggering immediate alerts while maintaining searchable incident records.\n\n---\n\n# 💡 Solution\n\nThe system continuously monitors live video feeds using YOLOv8.\n\nWhen a weapon is detected:\n\n- Detects the object\n- Captures an image\n- Records the video clip\n- Stores incident details\n- Sends SMS alerts\n- Updates the dashboard\n- Generates detection reports\n\n---\n\n# ✨ Features\n\n## 🤖 AI Detection\n\n- Real-time weapon detection\n- Custom YOLOv8 model\n- Live RTSP/IP Camera support\n- USB Camera support\n- Confidence-based detection\n\n## 🚨 Alert System\n\n- Twilio SMS alerts\n- Detection history\n- Alert logs\n- Timestamp recording\n\n## 📊 Dashboard\n\n- Incident analytics\n- Active alerts\n- Detection statistics\n- Weapon gallery\n- Interactive reports\n\n## 👤 Authentication\n\n- User Registration\n- Secure Login\n- Session Management\n\n## 📁 Reports\n\n- Detection reports\n- Captured Images\n- Video Evidence\n- Historical records\n\n## 🖥 Edge AI\n\n- Raspberry Pi compatible\n- Lightweight deployment\n- Local inference support\n\n---\n\n# 🏗 System Architecture\n\n```\n             Live Camera\n                  │\n                  ▼\n          YOLOv8 Detection\n                  │\n        ┌─────────┴──────────┐\n        ▼                    ▼\n Save Image \u0026 Video     Twilio SMS Alert\n        │                    │\n        ▼                    ▼\n    SQLite Database      Emergency Contact\n              │\n              ▼\n      Flask Web Dashboard\n              │\n              ▼\n     Reports \u0026 Analytics\n```\n\n---\n\n# 🛠 Technology Stack\n\n| Layer | Technology |\n|---------|------------|\n| Programming | Python |\n| Backend | Flask |\n| Frontend | HTML CSS JavaScript |\n| Computer Vision | OpenCV |\n| AI Model | YOLOv8 |\n| Database | SQLite |\n| Alerts | Twilio API |\n| Deployment | Raspberry Pi |\n| Version Control | Git \u0026 GitHub |\n\n---\n\n# 📂 Project Structure\n\n```\nFALCON/\n│\n├── screenshots/\n├── static/\n├── templates/\n├── train_detect_weapons/\n│\n├── app.py\n├── yolo_detect.py\n├── live_rtsp_detector.py\n├── train_yolo.py\n├── twilio_alert.py\n├── database.py\n├── alert_db.py\n│\n├── requirements.txt\n├── .gitignore\n└── README.md\n```\n\n---\n\n## Screenshots\n\n### Login\n![Login](screenshots/login.png)\n\n### Register\n![Register](screenshots/register.png)\n\n### Dashboard\n![Dashboard](screenshots/dashboard.png)\n\n### Detection Report\n![Report](screenshots/report.png)\n\n### Alert\n![Alert](screenshots/alert.png)\n\n---\n\n# ⚙ Installation\n\n```bash\ngit clone https://github.com/Astik97/FALCON.git\n\ncd FALCON\n```\n\nCreate virtual environment\n\n```bash\npython -m venv venv\n```\n\nWindows\n\n```bash\nvenv\\Scripts\\activate\n```\n\nLinux\n\n```bash\nsource venv/bin/activate\n```\n\nInstall dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n# 🔐 Environment Variables\n\nCreate a `.env` file.\n\n```env\nTWILIO_ACCOUNT_SID=\n\nTWILIO_AUTH_TOKEN=\n\nTWILIO_PHONE=\n\nSECRET_KEY=\n```\n\n---\n\n# ▶ Running the Project\n\n```bash\npython app.py\n```\n\nOpen\n\n```\nhttp://127.0.0.1:5000\n```\n\n---\n\n# 🔄 Workflow\n\n```\nStart Camera\n\n↓\n\nYOLOv8 Detection\n\n↓\n\nWeapon Detected\n\n↓\n\nCapture Evidence\n\n↓\n\nStore in Database\n\n↓\n\nSMS Alert\n\n↓\n\nDashboard Update\n\n↓\n\nGenerate Report\n```\n\n---\n\n# 📈 Model Performance\n\n| Metric | Value |\n|---------|--------|\n| Model | YOLOv8 |\n| Dataset | 9,633 Images |\n| Epochs | 50 |\n| Precision | 85% |\n| Recall | 75% |\n| mAP@0.5 | 81% |\n\n---\n\n# ⚡ Performance Benchmark\n\n| Device | FPS | Latency |\n|----------|------|----------|\n| GPU | 15–25 | 40–60 ms |\n| CPU | 5–10 | 120–200 ms |\n| Raspberry Pi | 1–3 | 400–800 ms |\n\n---\n\n# 🔒 Security\n\n- Environment variables for secrets\n- `.gitignore` excludes credentials\n- Secure authentication\n- Session management\n- No API keys committed\n\n---\n\n# ⚠ Limitations\n\n- Reduced accuracy in poor lighting\n- False positives possible\n- Raspberry Pi has limited inference speed\n- Stable network required for RTSP\n\n---\n\n# 🗺 Future Roadmap\n\n- Docker Support\n- PostgreSQL Migration\n- Multi-camera Monitoring\n- Email Alerts\n- Push Notifications\n- Cloud Deployment\n- ONNX/TensorRT Optimization\n- Mobile Dashboard\n- Face Recognition Module\n\n---\n\n# 🤝 Contributing\n\nContributions, issues, and feature requests are welcome.\n\nFork the repository.\n\nCreate a feature branch.\n\nCommit your changes.\n\nOpen a Pull Request.\n\n---\n\n# 📄 License\n\nThis project is licensed under the MIT License.\n\n---\n\n# 👨‍💻 Author\n\n## Astik Mohapatra\n\nBackend Developer • AI Systems Developer • Computer Vision Enthusiast\n\n📧 Email:\nastikm7007@gmail.com\n\n🔗 LinkedIn\n\nhttps://linkedin.com/in/astik-mohapatra\n\n🐙 GitHub\n\nhttps://github.com/Astik97\n\n---\n\n# ⭐ Support\n\nIf you found this project helpful,\n\n⭐ Star the repository\n\n🍴 Fork it\n\n📢 Share it\n\nHappy Coding 🚀","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAstik97%2FFALCON","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAstik97%2FFALCON","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAstik97%2FFALCON/lists"}