MWaste: A Deep Learning Approach to Manage Household Waste

TL;DR

MWaste is a mobile app leveraging deep learning and computer vision to accurately classify household waste, aiming to improve recycling efficiency and reduce environmental impact.

Contribution

The paper introduces MWaste, a novel mobile application that applies deep learning for waste classification, demonstrating high accuracy on real-world images.

Findings

Achieved 92% average precision in waste classification

Effective across various neural network architectures

Potential to improve recycling and reduce greenhouse gases

Abstract

Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92\% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.