AtomSentinel

## Overview

AtomSentinel addresses the Loading & Unloading Subproblem in nuclear facilities by combining advanced computer vision with Arduino-based sensing. The system enhances nuclear safety during material handling operations through automated detection and monitoring.

## Problem Statement

Nuclear material handling requires strict safety protocols and constant monitoring. Manual oversight can be error-prone and may miss critical safety violations or unauthorized personnel access.

## Solution

AtomSentinel uses YOLOv11 (You Only Look Once version 11) for real-time object detection and personnel identification. Arduino sensors monitor environmental conditions and material handling equipment, providing a comprehensive safety monitoring system.

## Features

- **Personnel Detection**: Real-time identification of unauthorized personnel in restricted areas
- **Nuclear Leak Monitoring**: Continuous monitoring of potential nuclear material leaks
- **Computer Vision Integration**: YOLOv11 for advanced object and person detection
- **Arduino Sensing**: Environmental and equipment monitoring
- **Safety Alerts**: Automated warnings for safety violations

## Technologies Used

- **AI/Computer Vision**: YOLOv11, OpenCV, Python
- **Hardware**: Arduino microcontrollers
- **Software**: Python for computer vision processing

## Challenges & Learnings

**Challenges:**
- Real-time computer vision processing
- Integration of multiple sensing modalities
- Ensuring reliability in critical safety applications

**Learnings:**
- Advanced computer vision model deployment
- Embedded systems integration
- Safety-critical system design principles