DRONAKSH

Tech Stack

Python

Java

IoT

HTML

CSS

JS

Drone

Twilio

Description

DRONAKSH is a swarm drone-based surveillance system powered by AI to ensure real-time threat detection in crowded environments.

It can:

Detect weapons, violence, smoke, and abnormal crowd behavior
Provide 360° aerial coverage, eliminating blind spots
Send instant alerts to authorities for faster response
Operate autonomously over large areas using smart coordination

Progress During Hackathon

24-Hour Hackathon Progress: Dronaksh Goal - Build an AI-powered drone system for real-time surveillance – crowd monitoring, fight detection, and smoking detection. Hardware (0–6 hrs) - Assembled custom drone (frame, motors, Pixhawk, telemetry, camera). - Configured autonomous path planning using *Mission Planner*. AI Integration (6–12 hrs) - *Crowd Monitoring*: YOLOv8 for real-time crowd detection. - *Fight Detection*: Action recognition model for violent activity. - *Smoking Detection*: Custom model to detect smoking behavior. System Integration (12–18 hrs) - Live drone video feed connected to AI pipeline. - Detections mapped with GPS and logged in real-time. Communication (18–22 hrs) - Built *WhatsApp chatbot* using Twilio: - Sends alerts (fight/smoke/crowd). - Shares drone location. - Supports admin commands. Final Testing (22–24 hrs) - Full demo: Autonomous drone flight + real-time detections + chatbot alerts. - Simulated scenarios tested successfully. Deliverables Achieved: ✔ Drone hardware + flight path planning ✔ 3 AI models integrated ✔ Real-time alerts via WhatsApp

Fundraising Status

Bootstrapped Idea

DRONAKSH

Videos

Tech Stack

Description

Progress During Hackathon

Fundraising Status