Future Work

Qt Application Development

• Implement proper Qt application architecture using the Model-View-Controller pattern
• Create modular components for better code organization and maintainability
• Add unit tests for all application functionality
• Improve error handling and system robustness

User Experience Enhancements

• Add user profiles and authentication for different access levels (operator, administrator)
• Implement customizable themes and accessibility features
• Create an admin dashboard for monitoring system performance
• Add multilingual support to accommodate diverse workforces

Additional Features

• Add the additional features that are shown in the pictures.
• Tracking PPE dispensing history
• Tracking PPE usage patterns
• Reporting and analytics integration with ROSBag uploads to AWS S3
• Ability to change detailed settings and timings.
• Overall proper logging and tracking of events such as Override, Dispensing, etc.

A significant area for enhancement is implementing robust data tracking and analytics capabilities:

Inventory Management

• Real-time inventory tracking of all PPE items
• Automated restocking notifications and supplier integration
• Consumption patterns analysis to optimize stock levels
• Integration with enterprise resource planning (ERP) systems

AWS Integration

• Cloud-based data storage using Amazon S3
• Serverless computing with AWS Lambda for scalable processing
• Real-time analytics dashboard using QuickSight
• IoT integration using AWS IoT Core for machine-to-machine communication

Compliance Reporting

• Automated compliance reports for safety audits
• Historical data analysis for identifying safety trends
• Department-specific PPE usage metrics
• Integration with safety management systems

Potential Employee Management System Integration

• Integration with an existing employee management system to track employee PPE usage and compliance, through facial recognition.

The PPE detection capabilities can be significantly improved through several AI enhancements:

Custom Dataset Development

• Creation of proprietary PPE detection datasets specific to the client's work environment
• Capture of data across various lighting conditions and environments
• Annotated datasets including all required PPE items with higher accuracy
• Gather more robust datasets from real-world environments to improve the accuracy of the trainedmodel.

Advanced AI Models

• Integration with large language models like GPT-4 for contextual understanding
• Development of custom model architectures optimized for PPE detection

Real-time Performance Optimization

• Model quantization for faster inference on edge devices
• Exploring specialized hardware (TPUs, VPUs) for deployment

This was the proof of concept of the project, so there are several hardware improvements that could be made:

Advanced Camera Systems

• Integration of depth-sensing cameras for 3D PPE detection
• Implementation of multiple camera angles for comprehensive monitoring, especially for items such as earplugs.
• Exploration of thermal imaging for additional safety monitoring
• Include lighting solutions to ensure that the camera will be seeing the users in a set environment.

Vending System Enhancements

• Custom-designed dispensing mechanisms optimized for specific PPE types
• Integration of robotic elements for more complex item handling
• Modular design allowing easy expansion of capacity and item types
• Implementation of weight sensors for verification of successful dispensing

Or even better, an actual exisiting PPE vending machine could be used, instead of trying to convert a snack vending machine.

Safety Gate Integration

• Professionally engineered safety gates meeting all relevant safety codes
• Integration with facility access control systems
• Redundant safety mechanisms to prevent failures
• Emergency override systems with proper authorization and logging