Deep Learning Approaches for Automated Waste Classification and Sorting

Effective waste management is critical for environmental sustainability in modern societies. Manual classification and sorting of waste streams is labour-intensive, error-prone, and poses health hazards. Recent advancements in deep neural networks provide new opportunities for automated waste characterization and segregation. This work develops a deep convolutional neural network architecture for classifying various materials commonly found in municipal solid waste. The model achieves over 93% accuracy in categorizing plastics, paper, metals, and glass items from images. The system is designed for edge deployment on low-cost hardware at waste sorting facilities. We implement a closed-loop simulation environment using robotic arms and conveyor belts to demonstrate automated segregation based on predicted waste categories. Results highlight viability for improved waste processing via integration of deep learning-enabled automation. Broader adoption could lead to reduced costs and environmental impact while increasing recycling rates