Comparative Analysis of Multiple Deep CNN Models for Waste Classification

TL;DR

This paper evaluates various deep CNN architectures for automatic waste classification to improve recycling efficiency, achieving up to 87.8% accuracy with ResNet18, aiming to automate waste segregation and reduce human effort.

Contribution

It compares multiple deep learning models for waste classification and demonstrates the effectiveness of fine-tuned ResNet18 in achieving high accuracy.

Findings

ResNet18 achieved 87.8% validation accuracy.

Deep CNNs can automate waste segregation effectively.

The approach reduces human intervention in waste management.

Abstract

Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuning of the ResNet18 Network, the best validation accuracy was found to be 87.8%.