🚀 SkySentry

AI-Powered Visual Verification Layer for Aerial Threat Detection

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🧠 Overview

SkySentry AI is a real-time aerial threat verification system designed to bridge the gap between raw radar detection and actionable intelligence.

Traditional radar systems struggle with:

• ❌ False positives (birds, clutter)
• ❌ Lack of classification
• ❌ Alert fatigue

✅ Our Solution

SkySentry introduces a Visual Verification Layer:

Radar Detection → Camera Feed → AI Classification → Threat Decision

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⚙️ System Pipeline

Radar Trigger (Simulated)
        ↓
Camera Feed (Image / Video)
        ↓
YOLOv10 Model (ONNX Runtime)
        ↓
Object Classification
        ↓
Threat Analysis
        ↓
Command Center UI

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🔥 Key Features

• 🎯 Precision Classification Detects drones, birds, and aircraft with confidence scoring

• ⚡ Real-Time Processing Optimized for low-latency inference

• 🧠 AI-Powered Decision Layer Converts detections into actionable threat insights

• 🖥️ Command Center UI Military-style dashboard for monitoring and control

• 🔗 Modular Architecture Easily integrates with radar, EO, or IR systems

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🧰 Tech Stack

🚀 Backend

• ☕ Spring Boot (Java)
• ONNX Runtime (Model Inference)

🤖 AI / ML

• YOLOv10
• PyTorch → ONNX Conversion

🎨 Frontend

• HTML5
• CSS3 (HUD-style UI)
• JavaScript

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📁 Project Structure

SkySentry/
│
├── backend-spring/     # Spring Boot backend
├── index.html          # Frontend dashboard
├── best.onnx           # Model for inference
├── best.pt             # Training model (reference)
├── .gitignore
└── README.md

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🚀 Getting Started

🔧 Backend Setup

cd backend-spring
mvn clean install
mvn spring-boot:run

Backend runs at:

http://localhost:8080

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🌐 Frontend Setup

Simply open:

index.html

in your browser

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🔌 API Endpoint

Analyze Frame

POST /analyze

Input:

• Image / Base64 Frame

Output:

{
  "detections": [
    {
      "class": "drone",
      "confidence": 0.98,
      "bbox": [x1, y1, x2, y2]
    }
  ]
}

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🧪 Model Workflow

Train Model → best.pt
        ↓
Convert to ONNX
        ↓
Deploy best.onnx

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🎯 Use Cases

• 🛡️ Defense Surveillance
• 🏟️ Stadium Security
• 🏭 Industrial Monitoring
• 🚁 Drone Detection Systems

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⚠️ Important Notes

• ONNX model is required for backend inference
• Ensure correct file path for best.onnx
• Backend must be running before frontend interaction

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👥 Team

• Pranjal — Frontend & System Design
• Savyam — Backend & AI Integration
• Aryan — Research & Strategy
• Naitik — Presentation & Design
• Vaibhavi — Data pipeline

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💡 Vision

Democratizing high-fidelity aerial defense systems using accessible AI and modern architectures.

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