Challenges in Hospital Waste Management

Improper hospital waste disposal leads to increased health risks, occupational hazards, infection transmission, and environmental damage. The environmental toll extends to climate change through ineffective waste disposal techniques, such as open burning and landfilling, releasing significant greenhouse gases. Incorrect disposal of medical waste endangers land and water bodies, contaminating essential resources.

Introducing the Solution

In response to these challenges, Team Tesseract proposes a comprehensive waste management solution that leverages AI, machine vision, deep learning, and cutting-edge technologies. The project aims to automate the classification and segregation of various types of hospital waste, ensuring the safety of healthcare workers, patients, and the environment.

Seven Primary Areas of Development

1. Data collection and preparation
2. Training the AI model
3. Machine vision and image analysis
4. Customization and adaptability
5. Integration with existing systems
6. Continuous improvement and monitoring

AI Model Utilization

The team utilizes YOLOv5 as the object detection framework, focusing on reducing model size and improving performance. The algorithm provides advanced anchor box mechanism, model scaling, and an improved training process.

Dataset and Training

The team utilizes YOLOv5 as the object detection framework, focusing on reducing model size and improving performance. The algorithm provides advanced anchor box mechanism, model scaling, and an improved training process.

Robotic Arm-Based System

A robotic arm efficiently carries out the waste sorting process using AI and machine vision technology. The disposed garbage is scanned, waste is separated into smaller parts, and each item is placed into its respective compartment by the robot arm. The system incorporates a sterilization process for hygiene and cleanliness standards.

Smart Bin Technology

The smart bin monitors and sorts waste at the time of disposal, using AI for efficient and accurate sorting. The system employs AI and computer vision to sort waste into various categories, and robotic automation places the waste into its respective compartments. When a compartment is full, the system sends a notification for user attention.

Environmental and Social Impact

The integration of AI-based garbage separation systems into hospital waste management contributes to improved waste segregation, reduced contamination, and diminished environmental pollution. AI enhances recycling rates and ensures the proper handling of hazardous waste, promoting sustainable practices. From a social perspective, AI systems enhance occupational safety for healthcare workers, improving working conditions and preventing contamination.

Conclusion

In essence, Team Tesseract's AI-based garbage separation system plays a pivotal role in promoting efficient waste management, safeguarding the environment, improving occupational safety, and enhancing public health and community well-being.