A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

Area of Science:

• Computer Science
• Environmental Science
• Engineering

Background:

• Rapid consumerism and packaging advancements have drastically altered waste composition.
• Current waste identification methods are slow and inaccurate, hindering effective waste management.
• Overflowing bins pose environmental risks, contaminating soil and ecosystems.

Purpose of the Study:

• To develop an efficient and accurate image classification model for identifying diverse waste types.
• To integrate this model into a smart bin system for automated waste segregation and management.
• To enhance waste disposal processes, reduce environmental pollution, and recover valuable resources.

Main Methods:

• A modified ResNeXt architecture with a novel "horizontal and vertical block" was developed for enhanced feature extraction.
• The model was trained and tested on a dataset of various waste images, including metal, trash, biodegradable, non-biodegradable, glass, and hazardous materials.
• A three-component smart bin system was designed, incorporating sensors, a stepper motor, solar power, and a Raspberry Pi for real-time waste classification and management.

Main Results:

• The modified ResNeXt model achieved a high classification accuracy of 98.9% for waste images.
• The integrated smart bin system demonstrated efficient segregation of biodegradable, non-biodegradable, and hazardous waste.
• The system successfully reduced manpower, saved time, and ensured proper garbage collection, contributing to a cleaner environment.

Conclusions:

• The proposed modified ResNeXt model offers superior performance for waste image classification compared to existing deep learning models.
• The smart bin system provides a practical and eco-friendly solution for mixed waste disposal and resource recovery.
• This technology has the potential to significantly improve urban waste management and promote a sustainable environment.